Search

Precision medicine aims to identify specific patient subgroups that may benefit the most from a particular treatment than the whole population. Existing definitions for the best subgroup in subgroup analysis are based on a single outcome and do not consider multiple outcomes; specifically, outcomes of different types. In this article, we introduce a definition for the best subgroup under a multiple‐outcome setting with continuous, binary, and censored time‐to‐event outcomes. Our definition provides a trade‐off between the subgroup size and the conditional average treatment effects (CATE) in the subgroup with respect to each of the outcomes while taking the relative contribution of the outcomes into account. We conduct simulations to illustrate the proposed definition. By examining the outcomes of urinary tract infection and renal scarring in the RIVUR clinical trial, we identify a subgroup of children that would benefit the most from long‐term antimicrobial prophylaxis. [ABSTRACT FROM AUTHOR]

The accurate estimation of prediction errors in time series is an important problem, which has immediate implications for the accuracy of prediction intervals as well as the quality of a number of widely used time series model selection criteria such as the Akaike information criterion. Except for simple cases, however, it is difficult or even impossible to obtain exact analytical expressions for one-step and multi-step predictions. This may be one of the reasons that, unlike in the independent case (see Efron, 2004), up to now there has been no fully established methodology for time series prediction error estimation. Starting from an approximation to the bias-variance decomposition of the squared prediction error, a method for accurate estimation of prediction errors in both univariate and multivariate stationary time series is developed in this article. In particular, several estimates are derived for a general class of predictors that includes most of the popular linear, nonlinear, parametric and nonparametric time series models used in practice, with causal invertible autoregressive moving average and nonparametric autoregressive processes discussed as lead examples. Simulations demonstrate that the proposed estimators perform quite well in finite samples. The estimates may also be used for model selection when the purpose of modelling is prediction. [ABSTRACT FROM AUTHOR]

Abstract–Data sharpening in kernel regression has been shown to be an effective method of reducing bias while having minimal effects on variance. Earlier efforts to iterate the data sharpening procedure have been less effective, due to the employment of an inappropriate sharpening transformation. In the present paper, an iterated data sharpening algorithm is proposed which reduces the asymptotic bias at each iteration, while having modest effects on the variance. The efficacy of the iterative approach is demonstrated theoretically and via a simulation study. Boundary effects persist and the affected region successively grows when the iteration is applied to local constant regression. By contrast, boundary bias successively decreases for each iteration step when applied to local linear regression. This study also shows that after iteration, the resulting estimates are less sensitive to bandwidth choice, and a further simulation study demonstrates that iterated data sharpening with data-driven bandwidth selection via cross-validation can lead to more accurate regression function estimation. Examples with real data are used to illustrate the scope of change made possible by using iterated data sharpening and to also identify its limitations. [ABSTRACT FROM AUTHOR]

AbstractOur goal is to develop a general strategy to decompose a random variable X into multiple independent random variables, without sacrificing any information about unknown parameters. A recent paper showed that for some well-known natural exponential families, X can be thinned into independent random variables X(1),…,X(K) , such that X=∑k=1KX(k) . These independent random variables can then be used for various model validation and inference tasks, including in contexts where traditional sample splitting fails. In this paper, we generalize their procedure by relaxing this summation requirement and simply asking that some known function of the independent random variables exactly reconstruct X. This generalization of the procedure serves two purposes. First, it greatly expands the families of distributions for which thinning can be performed. Second, it unifies sample splitting and data thinning, which on the surface seem to be very different, as applications of the same principle. This shared principle is sufficiency. We use this insight to perform generalized thinning operations for a diverse set of families. [ABSTRACT FROM AUTHOR]

Summary Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12–0.49, 0.15–0.42, and 0.25–0.56) and increased NRMSE (3.58–18.24%, 6.27–11.55%, and 7.0–33.12%) compared with nonspatial random cross‐validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications. [ABSTRACT FROM AUTHOR]

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice. [ABSTRACT FROM AUTHOR]

This study addressed the issue of determining multiple potential clusters with regularization approaches for the purpose of spatio-temporal clustering. The generalized lasso framework has flexibility to incorporate adjacencies between objects in the penalty matrix and to detect multiple clusters. A generalized lasso model with two L 1 penalties is proposed, which can be separated into two generalized lasso models: trend filtering of temporal effect and fused lasso of spatial effect for each time point. To select the tuning parameters, the approximate leave-one-out cross-validation (ALOCV) and generalized cross-validation (GCV) are considered. A simulation study is conducted to evaluate the proposed method compared to other approaches in different problems and structures of multiple clusters. The generalized lasso with ALOCV and GCV provided smaller MSE in estimating the temporal and spatial effect compared to unpenalized method, ridge, lasso, and generalized ridge. In temporal effects detection, the generalized lasso with ALOCV and GCV provided relatively smaller and more stable MSE than other methods, for different structure of true risk values. In spatial effects detection, the generalized lasso with ALOCV provided higher index of edges detection accuracy. The simulation also suggested using a common tuning parameter over all time points in spatial clustering. Finally, the proposed method was applied to the weekly Covid-19 data in Japan form March 21, 2020, to September 11, 2021, along with the interpretation of dynamic behavior of multiple clusters. [ABSTRACT FROM AUTHOR]

Applying analytic approximations for computing multivariate normal cumulative distribution functions has led to a substantial improvement in the estimability of mixed multinomial probit models, both in terms of accuracy and especially in terms of computation time. This paper makes a contribution by presenting a possible way to improve the accuracy of estimating mixed multinomial probit model covariances based on the idea of parameter selection using cross-validation. Comparisons to the MACML approach indicate that the proposed parameter selection approach is able to recover covariance parameters more accurately, even when there is a moderate degree of independence between the random coefficients. The approach also estimates parameters efficiently, with standard errors tending to be smaller than those of the MACML approach, which can be observed by means of a real data case. [ABSTRACT FROM AUTHOR]

Vector autoregressive (VAR) modelling is widely employed in psychology for time series analyses of dynamic processes. However, the typically short time series in psychological studies can lead to overfitting of VAR models, impairing their predictive ability on unseen samples. Cross‐validation (CV) methods are commonly recommended for assessing the predictive ability of statistical models. However, it is unclear how the performance of CV is affected by characteristics of time series data and the fitted models. In this simulation study, we examine the ability of two CV methods, namely,10‐fold CV and blocked CV, in estimating the prediction errors of three time series models with increasing complexity (person‐mean, AR, and VAR), and evaluate how their performance is affected by data characteristics. We then compare these CV methods to the traditional methods using the Akaike (AIC) and Bayesian (BIC) information criteria in their accuracy of selecting the most predictive models. We find that CV methods tend to underestimate prediction errors of simpler models, but overestimate prediction errors of VAR models, particularly when the number of observations is small. Nonetheless, CV methods, especially blocked CV, generally outperform the AIC and BIC. We conclude our study with a discussion on the implications of the findings and provide helpful guidelines for practice. [ABSTRACT FROM AUTHOR]

Due to the advantages of Electrocardiogram (ECG) signals, which are challenging to replicate yet easy to get, ECG-based identification has become a new path in biometric recognition research. These classic feature extraction techniques require Hand-crafted or feature-specific implications. The methods used for selection and integration of features, are time-consuming. The main objective of this study is develop deep learning approach to study the features of ECG data digital characteristics, thus saving a lot of signal pre-processing steps. This research proposed novel technique in X-wave recognition of ECG signal using max-min threshold technique and classification of ECG signal. This signal has been processed for noise removal and normalization. Then this processed signal has been used to recognize X-wave from ECG signal. From recognized X-wave, the ECG signal has been classified using Improved Support Vector Machine (ISVM). The QRS complex has been detected using Stacked Auto-Encoder with Neural Networks (SAENN). The study took raw ECG signals and entropy-based features evaluated from extracted QRS complexes. Exams are based on classifying heart disorders into two, five, and twenty classes. The experimental findings showed that our suggested model attained a high classification accuracy of 97%, precision of 89%, recall of 90%, F-1 score of 88%. [ABSTRACT FROM AUTHOR]

AbstractConformal prediction is a generic methodology for finite-sample valid distribution-free prediction. This technique has garnered a lot of attention in the literature partly because it can be applied with any machine learning algorithm that provides point predictions to yield valid prediction regions. Of course, the efficiency (width/volume) of the resulting prediction region depends on the performance of the machine learning algorithm. In the context of point prediction, several techniques (such as cross-validation) exist to select one of many machine learning algorithms for better performance. In contrast, such selection techniques are seldom discussed in the context of set prediction (or prediction regions). In this article, we consider the problem of obtaining the smallest conformal prediction region given a family of machine learning algorithms. We provide two general-purpose selection algorithms and consider coverage as well as width properties of the final prediction region. The first selection method yields the smallest width prediction region among the family of conformal prediction regions for all sample sizes but only has an approximate coverage guarantee. The second selection method has a finite sample coverage guarantee but only attains close to the smallest width. The approximate optimal width property of the second method is quantified via an oracle inequality. As an illustration, we consider the use of aggregation of nonparametric regression estimators in the split conformal method with the absolute residual conformal score. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Background: The timing of treating cancer patients is an essential factor in the efficacy of treatment. So, patients who will not respond to current therapy should receive a different treatment as early as possible. Machine learning models can be built to classify responders and nonresponders. Such classification models predict the probability of a patient being a responder. Most methods use a probability threshold of 0.5 to convert the probabilities into binary group membership. However, the cutoff of 0.5 is not always the optimal choice. Methods: In this study, we propose a novel data-driven approach to select a better cutoff value based on the optimal cross-validation technique. To illustrate our novel method, we applied it to three clinical trial datasets of small-cell lung cancer patients. We used two different datasets to build a scoring system to segment patients. Then the models were applied to segment patients into the test data. Results: We found that, in test data, the predicted responders and non-responders had significantly different long-term survival outcomes. Our proposed novel method segments patients better than the standard approach using a cutoff of 0.5. Comparing clinical outcomes of responders versus non-responders, our novel method had a p-value of 0.009 with a hazard ratio of 0.668 for grouping patients using the Cox proportion hazard model and a p-value of 0.011 using the accelerated failure time model which approved a significant difference between responders and non-responders. In contrast, the standard approach had a p-value of 0.194 with a hazard ratio of 0.823 using the Cox proportion hazard model and a p-value of 0.240 using the accelerated failure time model indicating the responders and non-responders do not differ significantly in survival. Conclusion: In summary, our novel prediction method can successfully segment new patients into responders and non-responders. Clinicians can use our prediction to decide if a patient should receive a different treatment or stay with the current treatment. [ABSTRACT FROM AUTHOR]

Bulked-segregant analysis by deep sequencing (BSA-seq) is a widely used method for mapping QTL (quantitative trait loci) due to its simplicity, speed, cost-effectiveness, and efficiency. However, the ability of BSA-seq to detect QTL is often limited by inappropriate experimental designs, as evidenced by numerous practical studies. Most BSA-seq studies have utilized small to medium-sized populations, with F2 populations being the most common choice. Nevertheless, theoretical studies have shown that using a large population with an appropriate pool size can significantly enhance the power and resolution of QTL detection in BSA-seq, with F3 populations offering notable advantages over F2 populations. To provide an experimental demonstration, we tested the power of BSA-seq to identify QTL controlling days from sowing to heading (DTH) in a 7200-plant rice F3 population in two environments, with a pool size of approximately 500. Each experiment identified 34 QTL, an order of magnitude greater than reported in most BSA-seq experiments, of which 23 were detected in both experiments, with 17 of these located near 41 previously reported QTL and eight cloned genes known to control DTH in rice. These results indicate that QTL mapping by BSA-seq in large F3 populations and multi-environment experiments can achieve high power, resolution, and reliability. [ABSTRACT FROM AUTHOR]

The generalized receiver-operating characteristic, gROC, curve considers the classification ability of diagnostic tests when both larger and lower values of the marker are associated with higher probabilities of being positive. Its empirical estimation implies to select the best classification subsets among those satisfying particular condition. Both strong and weak consistency have already been proved. However, using the same data for both to select the classification subsets and to calculate its gROC curve leads to an over-optimistic estimate of the real performance of the diagnostic criteria on future samples. In this work, the bias of the empirical gROC curve estimator is explored through Monte Carlo simulations. Besides, two cross-validation based algorithms are proposed for reducing the overfitting. The practical application of the proposed algorithms is illustrated through the analysis of a real-world dataset. Simulation results suggest that the empirical gROC curve estimator returns optimistic approximations, especially, in situations in which the diagnostic capacity of the marker is poor and the sample size is small. The new proposed algorithms improve the estimation of the actual diagnostic test accuracy, and get almost unbiased gAUCs in most of the considered scenarios. However, the cross-validation based algorithms reported larger L 1 -errors than the standard empirical estimators, and increment the computational cost of the procedures. As online supplementary material, this manuscript includes an R function which wraps up the implemented routines. [ABSTRACT FROM AUTHOR]

Ancestral sequence reconstruction (ASR) is a phylogenetic method widely used to analyze the properties of ancient biomolecules and to elucidate mechanisms of molecular evolution. Despite its increasingly widespread application, the accuracy of ASR is currently unknown, as it is generally impossible to compare resurrected proteins to the true ancestors. Which evolutionary models are best for ASR? How accurate are the resulting inferences? Here we answer these questions using a cross-validation method to reconstruct each extant sequence in an alignment with ASR methodology, a method we term "extant sequence reconstruction" (ESR). We thus can evaluate the accuracy of ASR methodology by comparing ESR reconstructions to the corresponding known true sequences. We find that a common measure of the quality of a reconstructed sequence, the average probability, is indeed a good estimate of the fraction of correct amino acids when the evolutionary model is accurate or overparameterized. However, the average probability is a poor measure for comparing reconstructions from different models, because, surprisingly, a more accurate phylogenetic model often results in reconstructions with lower probability. While better (more predictive) models may produce reconstructions with lower sequence identity to the true sequences, better models nevertheless produce reconstructions that are more biophysically similar to true ancestors. In addition, we find that a large fraction of sequences sampled from the reconstruction distribution may have fewer errors than the single most probable (SMP) sequence reconstruction, despite the fact that the SMP has the lowest expected error of all possible sequences. Our results emphasize the importance of model selection for ASR and the usefulness of sampling sequence reconstructions for analyzing ancestral protein properties. ESR is a powerful method for validating the evolutionary models used for ASR and can be applied in practice to any phylogenetic analysis of real biological sequences. Most significantly, ESR uses ASR methodology to provide a general method by which the biophysical properties of resurrected proteins can be compared to the properties of the true protein. [ABSTRACT FROM AUTHOR]

Novel features derived from imaging and artificial intelligence systems are commonly coupled to construct computer‐aided diagnosis (CAD) systems that are intended as clinical support tools or for investigation of complex biological patterns. This study used sulcal patterns from structural images of the brain as the basis for classifying patients with schizophrenia from unaffected controls. Statistical, machine learning and deep learning techniques were sequentially applied as a demonstration of how a CAD system might be comprehensively evaluated in the absence of prior empirical work or extant literature to guide development, and the availability of only small sample datasets. Sulcal features of the entire cerebral cortex were derived from 58 schizophrenia patients and 56 healthy controls. No similar CAD systems has been reported that uses sulcal features from the entire cortex. We considered all the stages in a CAD system workflow: preprocessing, feature selection and extraction, and classification. The explainable AI techniques Local Interpretable Model‐agnostic Explanations and SHapley Additive exPlanations were applied to detect the relevance of features to classification. At each stage, alternatives were compared in terms of their performance in the context of a small sample. Differentiating sulcal patterns were located in temporal and precentral areas, as well as the collateral fissure. We also verified the benefits of applying dimensionality reduction techniques and validation methods, such as resubstitution with upper bound correction, to optimize performance. [ABSTRACT FROM AUTHOR]

Utilization of small molecules as passivation materials for perovskite solar cells (PSCs) has gained significant attention recently, with hundreds of small molecules demonstrating passivation effects. In this study, a high‐accuracy machine learning model is established to identify the dominant molecular traits influencing passivation and efficiently screen excellent passivation materials among small molecules. To address the challenge of limited available dataset, a novel evaluation method called random‐extracted and recoverable cross‐validation (RE‐RCV) is proposed, which ensures more precise model evaluation with reduced error. Among 31 examined features, dipole moment is identified, hydrogen bond acceptor count, and HOMO‐LUMO gap as significant traits affecting passivation, offering valuable guidance for the selection of passivation molecules. The predictions are experimentally validate with three representative molecules: 4‐aminobenzenesulfonamide, 4‐Chloro‐2‐hydroxy‐5‐sulfamoylbenzoic acid, and Phenolsulfonphthalein, which exhibit capability to increase absolute efficiency values by over 2%, with a champion efficiency of 25.41%. This highlights its potential to expedite advancements in PSCs. [ABSTRACT FROM AUTHOR]

Background: Genomic selection (GS) is an efficient breeding strategy to improve quantitative traits. It is necessary to calculate genomic estimated breeding values (GEBVs) for GS. This study investigated the prediction accuracy of GEBVs for five fruit traits including fruit weight, fruit width, fruit height, pericarp thickness, and Brix. Two tomato germplasm collections (TGC1 and TGC2) were used as training populations, consisting of 162 and 191 accessions, respectively. Results: Large phenotypic variations for the fruit traits were found in these collections and the 51K Axiom™ SNP array generated confident 31,142 SNPs. Prediction accuracy was evaluated using different cross-validation methods, GS models, and marker sets in three training populations (TGC1, TGC2, and combined). For cross-validation, LOOCV was effective as k-fold across traits and training populations. The parametric (RR-BLUP, Bayes A, and Bayesian LASSO) and non-parametric (RKHS, SVM, and random forest) models showed different prediction accuracies (0.594–0.870) between traits and training populations. Of these, random forest was the best model for fruit weight (0.780–0.835), fruit width (0.791–0.865), and pericarp thickness (0.643–0.866). The effect of marker density was trait-dependent and reached a plateau for each trait with 768−12,288 SNPs. Two additional sets of 192 and 96 SNPs from GWAS revealed higher prediction accuracies for the fruit traits compared to the 31,142 SNPs and eight subsets. Conclusion: Our study explored several factors to increase the prediction accuracy of GEBVs for fruit traits in tomato. The results can facilitate development of advanced GS strategies with cost-effective marker sets for improving fruit traits as well as other traits. Consequently, GS will be successfully applied to accelerate the tomato breeding process for developing elite cultivars. [ABSTRACT FROM AUTHOR]

Tuning hyperparameters, such as the regularization parameter in Ridge or Lasso regression, is often aimed at improving the predictive performance of risk prediction models. In this study, various hyperparameter tuning procedures for clinical prediction models were systematically compared and evaluated in low‐dimensional data. The focus was on out‐of‐sample predictive performance (discrimination, calibration, and overall prediction error) of risk prediction models developed using Ridge, Lasso, Elastic Net, or Random Forest. The influence of sample size, number of predictors and events fraction on performance of the hyperparameter tuning procedures was studied using extensive simulations. The results indicate important differences between tuning procedures in calibration performance, while generally showing similar discriminative performance. The one‐standard‐error rule for tuning applied to cross‐validation (1SE CV) often resulted in severe miscalibration. Standard non‐repeated and repeated cross‐validation (both 5‐fold and 10‐fold) performed similarly well and outperformed the other tuning procedures. Bootstrap showed a slight tendency to more severe miscalibration than standard cross‐validation‐based tuning procedures. Differences between tuning procedures were larger for smaller sample sizes, lower events fractions and fewer predictors. These results imply that the choice of tuning procedure can have a profound influence on the predictive performance of prediction models. The results support the application of standard 5‐fold or 10‐fold cross‐validation that minimizes out‐of‐sample prediction error. Despite an increased computational burden, we found no clear benefit of repeated over non‐repeated cross‐validation for hyperparameter tuning. We warn against the potentially detrimental effects on model calibration of the popular 1SE CV rule for tuning prediction models in low‐dimensional settings. [ABSTRACT FROM AUTHOR]

This article considers the impact of different thresholding methods to the Nearest Shrunken Centroid algorithm, which is popularly referred as the Prediction Analysis of Microarrays (PAM) for high-dimensional classification. PAM uses soft thresholding to achieve high computational efficiency and high classification accuracy but in the price of retaining too many features. When applied to microarray human cancers, PAM selected 2611 features on average from 10 multi-class datasets. Such a large number of features make it difficult to perform follow up study. One reason behind this problem is the soft thresholding, which is known to produce biased parameter estimate in regression analysis. In this article, we extend the PAM algorithm with two other thresholding methods, hard and order thresholding, and a deep search algorithm to achieve better thresholding parameter estimate. The modified algorithms are extensively tested and compared to the original one based on real data and Monte Carlo studies. In general, the modification not only gave better cancer status prediction accuracy, but also resulted in more parsimonious models with significantly smaller number of features. [ABSTRACT FROM AUTHOR]

Intoduction/Aim: Extant tests of developmental theories have largely refrained from moving past testing models of association to building models of prediction, as have other fields with an intervention focus. With this in mind, we test the prognostic capacity to predict offending outcomes in early adulthood derived from various developmental theories. Methods: Using 734 subjects from the Rochester Youth Development Study (RYDS), we use out-of-sample predictions based on 5-fold cross-validation and compare the sensitivity, specificity and positive predictive value of three different prognostic models to predict arrest and serious, persistent offending in early adulthood. The first uses only predictors measures in early adolescence, the second uses dynamic trajectories of delinquency from ages 14–22, and the third uses a combination of the two. We further consider how early in adolescence the trajectory models calibrate prediction. Results: Both the early adolescent risk factor only model and the dynamic trajectory model were poor at prognosticating both arrest and persistent offending in early adulthood, which is manifest in the large rate of false positive cases. Conculsion: Furthermore, existing developmental theories would be well served to move beyond cataloging risk factors and draw more heavily on refinements, including a greater focus on human agency in life course patterns of offending. [ABSTRACT FROM AUTHOR]

Globally, lung and colon cancers are among the most prevalent and lethal tumors. Early cancer identification is essential to increase the likelihood of survival. Histopathological images are considered an appropriate tool for diagnosing cancer, which is tedious and error-prone if done manually. Recently, machine learning methods based on feature engineering have gained prominence in automatic histopathological image classification. Furthermore, these methods are more interpretable than deep learning, which operates in a "black box" manner. In the medical profession, the interpretability of a technique is critical to gaining the trust of end users to adopt it. In view of the above, this work aims to create an accurate and interpretable machine-learning technique for the automated classification of lung and colon cancers from histopathology images. In the proposed approach, following the preprocessing steps, texture and color features are retrieved by utilizing the Haralick and Color histogram feature extraction algorithms, respectively. The obtained features are concatenated to form a single feature set. The three feature sets (texture, color, and combined features) are passed into the Light Gradient Boosting Machine (LightGBM) classifier for classification. And their performance is evaluated on the LC25000 dataset using hold-out and stratified 10-fold cross-validation (Stratified 10-FCV) techniques. With a test/hold-out set, the LightGBM with texture, color, and combined features classifies the lung and colon cancer images with 97.72%, 99.92%, and 100% accuracy respectively. In addition, a stratified 10-fold cross-validation method also revealed that LightGBM's combined or color features performed well, with an excellent mean auc_mu score and a low mean multi_logloss value. Thus, this proposed technique can help histologists detect and classify lung and colon histopathology images more efficiently, effectively, and economically, resulting in more productivity. [ABSTRACT FROM AUTHOR]

In Selk, L., and Gertheiss, J. [(2022), 'Nonparametric Regression and Classification with Functional, Categorical, and Mixed Covariates', Advances in Data Analysis and Classification] a nonparametric prediction method for models with multiple functional and categorical covariates is introduced. The dependent variable can be categorical (binary or multi-class) or continuous, so that both classification and regression problems are considered. In the paper at hand the asymptotic properties of this method are studied. A uniform convergence rate for the regression / classification estimator is given. It is further shown that a data-driven least squares cross-validation method can asymptotically remove irrelevant noise variables automatically. [ABSTRACT FROM AUTHOR]

In recently conducted phase III trials in a rare disease area, patients received monthly treatment at a high dose of the drug, which targets to lower a specific biomarker level, closely associated with the efficacy endpoint, to around 10% across patients. Although this high dose demonstrated strong efficacy, treatments were withheld due to the reports of serious adverse events. Dosing in these studies were later resumed at a reduced dosage which targets to lower the biomarker level to 15%–35% across patients. Two questions arose after this disruption. The first is whether the efficacy of this revised regimen as measured by the reduction in annualized event rate is adequate to support the continuation of the development and the second is whether the potential bias due to the loss of patients during this dosing gap process can be gauged. To address these questions, we built a prediction model that quantitatively characterizes biomarker vs. endpoint relationship and predicts efficacy at the 15%–35% range of the biomarker level using the available data from the original high dose. This model predicts favorable event rate in the target biomarker level and shows that the bias due to the loss of patients is limited. These results support the continued development of the revised regimen, however, given the limitation of the data available, this prediction is planned to be validated further when data under the revised regimen become available. [ABSTRACT FROM AUTHOR]

Out-of-sample prediction is the acid test of predictive models, yet an independent test dataset is often not available for assessment of the prediction error. For this reason, out-of-sample performance is commonly estimated using data splitting algorithms such as cross-validation or the bootstrap. For quantitative outcomes, the ratio of variance explained to total variance can be summarized by the coefficient of determination or in-sample R2, which is easy to interpret and to compare across different outcome variables. As opposed to in-sample R2, out-of-sample R2 has not been well defined and the variability on out-of-sample R ̂ 2 has been largely ignored. Usually only its point estimate is reported, hampering formal comparison of predictability of different outcome variables. Here we explicitly define out-of-sample R2 as a comparison of two predictive models, provide an unbiased estimator and exploit recent theoretical advances on uncertainty of data splitting estimates to provide a standard error for R ̂ 2 . The performance of the estimators for R2 and its standard error are investigated in a simulation study. We demonstrate our new method by constructing confidence intervals and comparing models for prediction of quantitative Brassica napus and Zea mays phenotypes based on gene expression data. Our method is available in the R-package oosse. [ABSTRACT FROM AUTHOR]

Accurate reference evapotranspiration (ET0) estimation has an effective role in reducing water losses and raising the efficiency of irrigation water management. The complicated nature of the evapotranspiration process is illustrated in the amount of meteorological variables required to estimate ET0. Incomplete meteorological data is the most significant challenge that confronts ET0 estimation. For this reason, different machine learning techniques have been employed to predict ET0, but the complicated structures and architectures of many of them make ET0 estimation very difficult. For these challenges, ensemble learning techniques are frequently employed for estimating ET0, particularly when there is a shortage of meteorological data. This paper introduces a powerful super learner ensemble technique for ET0 estimation, where four machine learning models: Extra Tree Regressor, Support Vector Regressor, K-Nearest Neighbor and AdaBoost Regression represent the base learners and their outcomes used as training data for the meta learner. Overcoming the overfitting problem that affects most other ensemble methods is a significant advantage of this cross-validation theory-based approach. Super learner performances were compared with the base learners for their forecasting capabilities through different statistical standards, where the results revealed that the super learner has better accuracy than the base learners, where different combinations of variables have been used whereas Coefficient of Determination (R2) ranged from 0.9279 to 0.9994 and Mean Squared Error (MSE) ranged from 0.0026 to 0.3289 mm/day but for the base learners R2 ranged from 0.5592 to 0.9977, and MSE ranged from 0.0896 to 2.0118 mm/day therefore, super learner is highly recommended for ET0 prediction with limited meteorological data. [ABSTRACT FROM AUTHOR]

The presence of foreign objects in rice collections is known to pose significant concerns for both food safety and product quality. Neglecting to address these issues can result in a deterioration of rice quality, a decline in its economic value, and a loss of consumer trust and confidence. Therefore, this research introduced a novel model for the recognition of natural and manufactured foreign objects through the use of Deep Convolutional Neural Network (DCNN). The model focused on analyzing images of rice for easy identification and classification. DCNN model classified the images into six groups based on different types, namely stone, paddy, fragment rice, broken, broken-yellow, and black-red. These classifications were determined using three pre-trained models, such as ResNet50, VGG16, and MobileNetV2. This research also used comparative techniques for multi-class classifications, combining multiple machine learning with digital image processing measurements and comparative performance. Despite using a cross-validation model, the experiment proved that using a pre-trained technique with transfer learning produced higher accuracy than a typical machine learning model. The most accurate prediction was made by VGG16 using a transfer learning of 97% compared to a random forest (RF) value of 94% without cross-validation with 5 K-fold and 10 K-fold cross-validations. [ABSTRACT FROM AUTHOR]

Ridge regression is one of the most popular shrinkage estimation methods for linear models. Ridge regression effectively estimates regression coefficients in the presence of high-dimensional regressors. Recently, a generalized ridge estimator was suggested that involved generalizing the uniform shrinkage of ridge regression to non-uniform shrinkage; this was shown to perform well in sparse and high-dimensional linear models. In this paper, we introduce our newly developed R package "g.ridge" (first version published on 7 December 2023) that implements both the ridge estimator and generalized ridge estimator. The package is equipped with generalized cross-validation for the automatic estimation of shrinkage parameters. The package also includes a convenient tool for generating a design matrix. By simulations, we test the performance of the R package under sparse and high-dimensional settings with normal and skew-normal error distributions. From the simulation results, we conclude that the generalized ridge estimator is superior to the benchmark ridge estimator based on the R package "glmnet". Hence the generalized ridge estimator may be the most recommended estimator for sparse and high-dimensional models. We demonstrate the package using intracerebral hemorrhage data. [ABSTRACT FROM AUTHOR]

Behavioural asymmetries displayed by individuals, such as hand preference and foot preference, tend to be lateralized in the same direction (left or right). This may be because their co-ordination conveys functional benefits for a variety of motor behaviours. To explore the potential functional relationship between key motor asymmetries, we examined whether footedness, handedness, or throwing arm was the strongest predictor of eyedness. Behavioural asymmetries were measured by self-report in 578 left-handed and 612 right-handed individuals. Cluster analysis of the asymmetries revealed four handedness groups: consistent right-handers, left-eyed right-handers, consistent left-handers, and inconsistent left-handers (who were left-handed but right-lateralized for footedness, throwing and eyedness). Supervised machine learning models showed the importance of footedness, in addition to handedness, in determining eyedness. In right-handers, handedness was the best predictor of eyedness, followed closely by footedness, and for left-handers it was footedness. Overall, predictors were more informative in predicting eyedness for individuals with consistent lateral preferences. Implications of the findings in relation to the origins and genetics of handedness and sports training are discussed. Findings are related to fighting theories of handedness and to bipedalism, which evolved after manual dexterity, and which may have led to some humans being right-lateralized for ballistic movements and left-lateralized for hand dexterity. [ABSTRACT FROM AUTHOR]

AbstractTo overcome computational bottlenecks of various data perturbation procedures such as the bootstrap and cross-validations, we propose the Generative Multi-purpose Sampler (GMS), which directly constructs a generator function to produce solutions of weighted M-estimators from a set of given weights and tuning parameters. The GMS is implemented by a single optimization procedure without having to repeatedly evaluate the minimizers of weighted losses, and is thus capable of significantly reducing the computational time. We demonstrate that the GMS framework enables the implementation of various statistical procedures that would be unfeasible in a conventional framework, such as iterated bootstrap procedures and cross-validation for penalized likelihood. To construct a computationally efficient generator function, we also propose a novel form of neural network called the weight multiplicative multilayer perceptron to achieve fast convergence. An R package called GMS is provided, which runs under Pytorch to implement the proposed methods and allows the user to provide a customized loss function to tailor to their own models of interest. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

The concept of forest productivity is biologically complex. In forest masses, it is indirectly estimated via dominant height growth models and site indices. The aim of this study was to develop a dominant height model for Gmelina arborea using data from 129 monitoring plots. 9 dynamic equations were generated from the Hossfeld and Bertalanffy-Richards theoretical models, which were individually fitted and cross-validated. Among the evaluated models, the Bertalanffy-Richards model (parameter c) stood out. This model showed a logical biological trend, with polymorphic curves and a unique asymptote. The selected model is an important tool for the management of G. arborea forest plantations in areas with site characteristics similar to those of this study. [ABSTRACT FROM AUTHOR]

The northern part of the East African Rift System is characterized by depleted Moho depth and thermally thinned lithosphere. This research aims to determine the Moho depth of the study area through non-linear gravity inversion and cross-validation with seismic Moho estimates. The study utilized gravity data to obtain the gravity anomaly of the Moho interface, a topographic grid for removing topographic effects, a crustal model to determine total sediment thickness and its gravitational effect, and seismic Moho depth for constraining the forward model and cross-validation. The estimated Moho depth of the study area ranges between 5 km (in the Indian Ocean) to 45 km (in the Ethiopian Highlands), with slight variation compared to seismic Moho relief. This is because the reference level, calculated for the thinner part of the study region, underestimates the entire area. Upwelling magma in the Eastern branches of the EARS may also incur slight variation in the estimated Moho depth; rifting, volcanism, melt intrusion, magmatic uplift, and tectonic setting all influence the Moho depth of the study area. Furthermore, reverberations affect most seismic Moho estimations in the region. The slight variation can be mitigated by improving the gravity network for accurate validation and precise heat flow measurement to correctly identify magmatic anomalies and density contrasts. Additionally, applying reverberation removal techniques in the study region could improve seismic Moho estimation. [ABSTRACT FROM AUTHOR]

In this study, we developed a simple technique for effective parameter estimation and prediction of the quantitative structure activity relationship studies using a two‐step procedure. The first step is to choose the important molecular descriptors using the random forest regression, and the second step is to optimally predict the biological activity of the selected chemical compounds using the following estimators: ridge regression, jackknife ridge, Liu regression, jackknife Liu, Kibria–Lukman, and jackknife Kibria–Lukman. We conducted a simulation study and a real‐life analysis with a quantitative structure–activity relationship (QSAR) data with 2540 descriptors after preprocessing. The optimal prediction is determined using the cross‐validation error. The estimator with minimum cross‐validation error is considered best. It is obvious that performing jackknife estimation after random forest selection is preferred. In this study, we developed a simple technique for effective parameter estimation and prediction of the quantitative structure activity relationship studies (QSAR) using a two‐step procedure. We conducted a simulation study and a real‐life application with QSAR data with 2540 descriptors after preprocessing. The optimal prediction is determined using the cross‐validation error. The performance of the methods is judged using the root mean squared error of prediction. It is obvious that performing jackknife estimation after random forest selection is preferred. [ABSTRACT FROM AUTHOR]

Researchers have a growing interest in measuring the role of bystanders in cyberbullying. Two independent studies with Mexican adolescents (Sample 1 and Sample 2; N1 = 612, N2 = 612) were used to analyze the psychometric properties of the Styles of Bystander Defender Intervention Scale (SBDI) in adolescents. In two samples, confirmatory factorial analyses revealed that a first-order two-dimensional factor structure comprising constructive and aggressive intervention factors was equivalent. The results demonstrated configural, metric, and scalar measurement invariance in the SBDI across gender and education level (secondary vs. high school). Latent mean comparisons indicate differences by gender and education level in the model dimensions. Finally, the results indicate that defenders' aggressive interventions are positively correlated with cyberbullying and negatively associated with moral identity. On the other hand, constructive interventions were negatively related to cyberbullying and positively related to moral identity. The findings suggest that the SBDI is a helpful measure of the styles of bystander defender intervention in cyberbullying events. [ABSTRACT FROM AUTHOR]

Air quality is an important environmental component that has a significant influence on public health and well-being. Poor air quality can cause a variety of health problems, including respiratory and cardiovascular disorders. Therefore, there is a growing demand for air quality prediction tools to enable consumers and authorities to take the best decisions and to implement the necessary actions to reduce air pollution. The present paper describes an innovative application that uses machine learning techniques to supply to the users real-time air quality predictions made on past data from their unique location. The Scikit-learn Python package was used to implement five machine learning algorithms, including K-Nearest Neighbors, Random Forest, Gradient Boosting, Support Vector Regression (SVR) and AdaBoost. To achieve robust model performance, compatibility with cross-validation approaches was evaluated. The obtained results indicate that these machine learning techniques are successful at forecasting air quality. The AdaBoost method emerged as the best accurate predictor after extensive investigation, closely followed by Gradient Boosting, SVR, Random Forest, and KNearest Neighbors. Furthermore, the investigation also focused on the adapted handling of inaccurate data and providing graphical visualizations to highlight the algorithm's efficacy. [ABSTRACT FROM AUTHOR]

Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi‐ or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within‐time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site‐specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long‐term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states. [ABSTRACT FROM AUTHOR]

A data driven method based on generalized additive modelling (GAM) has been developed for homogenizing daily minimum and maximum temperature (TN, TX) series using parallel measurements and covariates. The method is applied to two coastal and two inland stations in the Netherlands. Between 1950 and 1972, these stations were relocated from cities to airports, accompanied by parallel measurement of at least 5 years at the old and new sites. Separating these parallel measurements in training and test data, the method compares numerous models involving covariates like the wind vector, cloudiness, specific humidity and sea surface temperature, and selects a model for each station. The resulting models offer an improvement compared to models based on temperature and season only: seasonal dependence is largely replaced by dependence on physical quantities. However, quantitatively, the impact is not large in the cases studied. One of the reasons might be that some covariates have only been measured at specific times not coinciding with the occurrences of the temperature minima or maxima. Additional benefits of the method are robustness and estimation of the sampling error variance of the daily homogenized daily temperature values. [ABSTRACT FROM AUTHOR]

Time-split cross-validation is broadly recognized as the gold standard for validating predictive models intended for use in medicinal chemistry projects. Unfortunately this type of data is not broadly available outside of large pharmaceutical research organizations. Here we introduce the SIMPD (simulated medicinal chemistry project data) algorithm to split public data sets into training and test sets that mimic the differences observed in real-world medicinal chemistry project data sets. SIMPD uses a multi-objective genetic algorithm with objectives derived from an extensive analysis of the differences between early and late compounds in more than 130 lead-optimization projects run within the Novartis Institutes for BioMedical Research. Applying SIMPD to the real-world data sets produced training/test splits which more accurately reflect the differences in properties and machine-learning performance observed for temporal splits than other standard approaches like random or neighbor splits. We applied the SIMPD algorithm to bioactivity data extracted from ChEMBL and created 99 public data sets which can be used for validating machine-learning models intended for use in the setting of a medicinal chemistry project. The SIMPD code and simulated data sets are available under open-source/open-data licenses at github.com/rinikerlab/molecular_time_series. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most critical diseases suffered by many people around the world, making it the most common medical risk they will face. This disease is considered the leading cause of death around the world, and early detection is difficult. In the field of healthcare, where early diagnosis based on machine learning (ML) helps save patients’ lives from the risks of diseases, better-performing diagnostic procedures are crucial. ML models have been used to improve the effectiveness of early diagnosis. In this paper, we proposed a new feature selection method that combines two filter methods, Pearson correlation and mutual information (PC-MI), to analyse the correlation amongst features and then select important features before passing them to a classification model. Our method is capable of early breast cancer prediction and depends on a soft voting classifier that combines a certain set of ML models (decision tree, logistic regression and support vector machine) to produce one model that carries the strengths of the models that have been combined, yielding the best prediction accuracy. Our work is evaluated by using the Wisconsin Diagnostic Breast Cancer datasets. The proposed methodology outperforms previous work, achieving 99.3% accuracy, an F1 score of 0.9922, a recall of 0.9846, a precision of 1 and an AUC of 0.9923. Furthermore, the accuracy of 10-fold cross-validation is 98.2%. [ABSTRACT FROM AUTHOR]

The Vietnamese government has established 37automatic ground air quality stations in three northern provinces, including the capital Hanoi, Bac Ninh, and Bac Giang, to provide hourly averaged PM2.5 data. However, these stations are still sparsely and unevenly distributed. This study seeks to develop an approach utilizing advanced machine learning models to forecast PM2.5 air pollution, with a specific focus on Bac Ninh Province in Vietnam. Historical relationship between the independent variable (input variable) and the dependent variable (target variable) to construct a time series model for PM2.5 prediction. The research findings reveal that the AutoARIMA model demonstrates superior performance, exhibiting better accuracy compared to other models (R² = 0.81, MSE = 3.5, MAE = 23.24, and RMSE = 0.17). The concentration of PM2.5 dust in Bac Ninh Province reaches a sensitive level that poses a threat to human health (100 micrograms/m³). Thuan Thanh, a southern district of Bac Ninh Province, registers the highest pollution level in the province, with a dust concentration value of 110 micrograms/m³. The research methodology is scientifically contributing to raising public awareness about air quality for both individuals and local government stakeholders. [ABSTRACT FROM AUTHOR]

When comparing multiple models of species distribution, models yielding higher predictive performance are clearly to be favored. A more difficult question is how to decide whether even the best model is "good enough". Here, we clarify key choices and metrics related to evaluating the predictive performance of presence–absence models. We use a hierarchical case study to evaluate how four metrics of predictive performance (AUC, Tjur's R2, max‐Kappa, and max‐TSS) relate to each other, the random and fixed effects parts of the model, the spatial scale at which predictive performance is measured, and the cross‐validation strategy chosen. We demonstrate that the very same metric can achieve different values for the very same model, even when similar cross‐validation strategies are followed, depending on the spatial scale at which predictive performance is measured. Among metrics, Tjur's R2 and max‐Kappa generally increase with species' prevalence, whereas AUC and max‐TSS are largely independent of prevalence. Thus, Tjur's R2 and max‐Kappa often reach lower values when measured at the smallest scales considered in the study, while AUC and max‐TSS reaching similar values across the different spatial levels included in the study. However, they provide complementary insights on predictive performance. The very same model may appear excellent or poor not only due to the applied metric, but also how predictive performance is exactly calculated, calling for great caution on the interpretation of predictive performance. The most comprehensive evaluation of predictive performance can be obtained by evaluating predictive performance through the combination of measures providing complementary insights. Instead of following simple rules of thumb or focusing on absolute values, we recommend comparing the achieved predictive performance to the researcher's own a priori expectations on how easy it is to make predictions related to the same question that the model is used for. [ABSTRACT FROM AUTHOR]

Timely screening and surveillance of children for developmental delay and social–emotional learning difficulties are essential in Low- and Middle-Income Countries like India. Screening measures like the Parents' Evaluation of Developmental Status (PEDS) and Strength and Difficulties Questionnaire (SDQ) are considered suitable for India due to their low cost, easy accessibility, and no training requirement for administration. However, India lacks validated screening measures, and the PEDS and SDQ have yet to be validated for children in India. The study aimed to translate the PEDS and SDQ from English to Hindi and psychometrically evaluate the same measures on children aged 4–8 years in India. The original PEDS and SDQ forms and their translations were pilot tested on 55 participants and evaluated using data from 407 children with typical development (TD) and 59 children with developmental disability (DD). Parents and teachers reported no meaningful discrepancy between the original and translated (Hindi) questionnaires. Internal consistency for the PEDS was acceptable, but unacceptable for most subscales on the SDQ, for both TD and DD samples. Test–retest reliability was poor for the PEDS but adequate for the SDQ. Results from known-group validity testing showed that the PEDS scores could be used to distinguish between the TD and DD samples. The results from this study provide further support for the use of the PEDS and SDQ in developing countries like India. [ABSTRACT FROM AUTHOR]

For urban rail transit (URT) operating companies, short-time passenger flow forecasting is a complex and critical task that determines the formulation and arrangement of operation plans and timetables. The automatic fare collection (AFC) system of URT provides detailed passenger flow data, which supports short-time passenger flow forecasting. This study proposes a combined model of Chaotic Sparrow Search Algorithm and Long Short-term memory artificial neural network (CSSA-LSTM) based on station classification. The model classifies stations based on various indicators such as point of interest (POI) data in the radiation area of Hangzhou, utilizes complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to denoise and smooth passenger flow data. It also conducts short-time passenger flow prediction and cross-validation for different stations by using the proposed model. To demonstrate the accuracy of the model, evaluation metrics such as R-squared and RMSE are introduced, and the results of the CSSA-LSTM model are compared with those of the LSTM, PSO-LSTM, and SSA-LSTM models. The experimental results show that the CSSA-LSTM model can effectively improve prediction accuracy, with R-squared increasing by 14.80%, 8.60%, and 6.82% compared to the other three algorithms, respectively. In addition, the cross-validation results of different stations prove the wide applicability of the CSSA-LSTM model, and this study have practical significance for URT planning and management. [ABSTRACT FROM AUTHOR]

The use of multivariate adaptive regression splines (MARS) and classification and regression tree (CART) to estimate the live body weight at weaning age of the Sussex cattle breed remain poorly understood in South Africa. This study was conducted to examine the effect of linear body measurements on body weight at weaning using MARS and CART algorithms. The body weight and linear body measurements included sternum height, withers height, heart girth, hip height, body length, rump length and rump width were collected from 101 Sussex cattle (female = 57 and male = 44) at weaning. Goodness of fit criterions was used to select the best data mining algorithms. The results showed that MARS showed higher predictive performance in the criteria as compared to CART algorithm. The findings of the study suggest that MARS algorithm can be used to estimate the BW at weaning age in Sussex cattle breed. These findings might be helpful to cattle farmers in the selection criterions of breeding stock at weaning age. [ABSTRACT FROM AUTHOR]

Variable selection is an important step to end up with good prediction models. LASSO regression models are one of the most commonly used methods for this purpose, for which cross‐validation is the most widely applied validation technique to choose the tuning parameter (λ). Validation techniques in a complex survey framework are closely related to "replicate weights". However, to our knowledge, they have never been used in a LASSO regression context. Applying LASSO regression models to complex survey data could be challenging. The goal of this paper is twofold. On the one hand, we analyze the performance of replicate weights methods to select the tuning parameter for fitting LASSO regression models to complex survey data. On the other hand, we propose new replicate weights methods for the same purpose. In particular, we propose a new design‐based cross‐validation method as a combination of the traditional cross‐validation and replicate weights. The performance of all these methods has been analyzed and compared by means of an extensive simulation study to the traditional cross‐validation technique to select the tuning parameter for LASSO regression models. The results suggest a considerable improvement when the new proposal design‐based cross‐validation is used instead of the traditional cross‐validation. [ABSTRACT FROM AUTHOR]

To evaluate conservation interventions, it is necessary to obtain reliable population trends for short (<10 years) time scales. Telemetry can be used to estimate short‐term survival rates and is a common tool for assessing population trends, but it has limitations and can be biased toward specific behavioral traits of tagged individuals. Encounter rates calculated from transects can be useful for assessing changes across multiple species, but they can have large confidence intervals and be affected by variations in survey conditions. The decline of African vultures has been well‐documented, but understanding of recent trends is lacking. To examine population trends, we used survival estimates from telemetry data collected over 6 years (primarily for white‐backed vultures [Gyps africanus]) and transect counts conducted over 8 years (for 7 scavenging raptors) in 3 large protected areas in Tanzania. Population trends were estimated using survival analysis combined with the Leslie Lefkovitch matrix model from the telemetry data and using Bayesian mixed effects generalized linear regression models from the transect data. Both methods showed significant declines for white‐backed vultures in Ruaha and Nyerere National Parks. Only telemetry estimates suggested significant declines in Katavi National Park. Encounter rates calculated from transects also showed declines in Nyerere National Park for lappet‐faced vultures (38% annual declines) and Bateleurs (18%) and in Ruaha National Park for white‐headed vultures (Trigonoceps occipitalis) (19%). Mortality rates recorded and inferred from telemetry suggested that poisoning is prevalent. However, only 6 mortalities of the 26 presumed mortalities were confirmed to be caused by poisoning, highlighting the challenges of determining the cause of death when working across large landscapes. Despite declines, our data provide evidence that southern Tanzania has higher current encounter rates of African vultures than elsewhere in East Africa. Preventing further declines will depend greatly on mitigating poisoning. Based on our results, we suggest that the use of multiple techniques improves understanding of population trends over the short term. [ABSTRACT FROM AUTHOR]

The paper considers parameter estimation in count data models using penalized likelihood methods. The motivating data consists of multiple independent count variables with a moderate sample size per variable. The data were collected during the assessment of oral reading fluency (ORF) in school-aged children. A sample of fourth-grade students were given one of ten available passages to read with these differing in length and difficulty. The observed number of words read incorrectly (WRI) is used to measure ORF. Three models are considered for WRI scores, namely the binomial, the zero-inflated binomial, and the beta-binomial. We aim to efficiently estimate passage difficulty, a quantity expressed as a function of the underlying model parameters. Two types of penalty functions are considered for penalized likelihood with respective goals of shrinking parameter estimates closer to zero or closer to one another. A simulation study evaluates the efficacy of the shrinkage estimates using Mean Square Error (MSE) as metric. Big reductions in MSE relative to unpenalized maximum likelihood are observed. The paper concludes with an analysis of the motivating ORF data. [ABSTRACT FROM AUTHOR]

Some biomedical datasets contain a small number of samples which have large numbers of features. This can make analysis challenging and prone to errors such as overfitting and misinterpretation. To improve the accuracy and reliability of analysis in such cases, we present a tutorial that demonstrates a mathematical approach for a supervised two-group classification problem using two medical datasets. A tutorial provides insights on effectively addressing uncertainties and handling missing values without the need for removing or inputting additional data. We describe a method that considers the size and shape of feature distributions, as well as the pairwise relations between measured features as separate derived features and prognostic factors. Additionally, we explain how to perform similarity calculations that account for the variation in feature values within groups and inaccuracies in individual value measurements. By following these steps, a more accurate and reliable analysis can be achieved when working with biomedical datasets that have a small sample size and multiple features. [ABSTRACT FROM AUTHOR]

Drought is a complex natural hazard that affects various regions of the world, causing significant economic and environmental losses. Accurate and timely monitoring and forecasting of drought conditions are essential for mitigating their impacts and enhancing resilience. Satellite-based drought indices have the advantage of providing spatially continuous and consistent information on drought severity and extent. A new drought product was developed from the thermal infrared observations of the Fengyun (FY) series of satellites. We proposed a data fusion algorithm to combine multiple FY satellites, including FY-2F, FY-2G, and FY-4A, to create a long time series of a land surface temperature (LST) data set without systematic bias. An FY drought index (FYDI) is then derived by coupling the long-term LST data set with the surface–atmospheric energy exchange model at 4 km spatial resolution over China from 2013 to present. The performance and reliability of the new FYDI product are evaluated in this study by comparing it with the Meteorological-drought Composite Index (MCI), one of the authoritative drought monitoring indices used in the Chinese meteorological services. The main objectives of this paper are: (1) to evaluate the performance of the FYDI in capturing the spatiotemporal patterns of drought events over China; (2) to quantitively analyze the consistency between the FYDI and MCI products; and (3) to explore the advantages and limitations of the FYDI for drought monitoring and assessment. The preliminary results show that the FYDI product has good agreement with the MCI, indicating that the FYDI can effectively identify the occurrence, duration, severity, and frequency of drought events over China. These two products have a strong correlation in terms of drought detection, with a correlation coefficient of approximately 0.7. The FYDI was found to be particularly effective in the regions where ground observation is scarce, with the capability of reflecting the spatial heterogeneity and variability of drought patterns more clearly. Overall, the FYDI can be a useful measure for operational drought monitoring and early warning, complementing the existing ground-based MCI drought indices. [ABSTRACT FROM AUTHOR]

AbstractIn the big data era, subsampling or sub-data selection techniques are often adopted to extract a fraction of informative individuals from the massive data. Existing subsampling algorithms focus mainly on obtaining a representative subset to achieve the best estimation accuracy under a given class of models. In this article, we consider a semi-supervised setting wherein a small or moderate sized “labeled” data is available in addition to a much larger sized “unlabeled” data. The goal is to sample from the unlabeled data with a given budget to obtain informative individuals that are characterized by their unobserved responses. We propose an optimal subsampling procedure that is able to maximize the diversity of the selected subsample and control the false selection rate (FSR) simultaneously, allowing us to explore reliable information as much as possible. The key ingredients of our method are the use of predictive inference for quantifying the uncertainty of response predictions and a reformulation of the objective into a constrained optimization problem. We show that the proposed method is asymptotically optimal in the sense that the diversity of the subsample converges to its oracle counterpart with FSR control. Numerical simulations and a real-data example validate the superior performance of the proposed strategy. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]