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Null hypothesis significance testing is generalized by controlling the Type I error rate conditional on the existence of a non-empty confidence interval. The control of that conditional error rate corrects p-values by transforming them into c-values. A further generalization from point null hypotheses to composite hypotheses generates possibility measures called C-values. The framework has implications for the following areas of application in addition to that of bounded parameter spaces. First, C-values of unspecified catch-all hypotheses provide conditions under which the entire statistical model would be rejected. Second, the C-value of a point estimate or confidence interval from a previous study determines whether the conclusion of the study is replicated, discredited, or neither replicated nor discredited by a new study. Third, c-values of a finite number of hypotheses, theories, or other models facilitate both incorporating previous information into frequentist hypothesis testing and the comparison of scientific models such as those of molecular evolution. In all cases, the corrections of p-values are simple enough to be performed on a handheld device. [ABSTRACT FROM AUTHOR]

The article presents the R programming language series, which explores probability distributions and introduces statistical tests. It mentions tests, including the Chi-Square test to determine the correlation between two categorical variables, the Beta function mathematically defined by the integral for two inputs, alpha and beta; and the Gamma distribution, which has a shape (z) and scale parameter (k).

Lloyd's heuristic K-Means, one of the most widely used clustering methods, plays a vital role in a variety of downstream tasks in machine learning owing to its simplicity. However, Lloyd's heuristic sometimes performs poorly in finding the local minimum and is heavily influenced by the initial points. To address these issues, we propose a novel optimization method for the K-Means model. First, we establish that the K-Means minimization problem can be reformulated as a trace maximization problem, which can be seen as a unified view of spectral clustering. Then, we relax the constraint of the scaled cluster matrix and implement an improved spectral rotation to bring the cluster matrix infinitely close to the binary indicator matrix. To this end, an efficient and redundancy-free coordinate descent (CD) method is used to optimize the spectral rotation. Extensive experiments including a hybrid test on several different datasets showed that the proposed algorithm achieved better local objective values compared to Lloyd's heuristic under different initialization strategies (random or K-Means++). In the hybrid test, the proposed algorithm could further decrease the convergence value of the objective function obtained by Lloyd's heuristic; conversely, Lloyd's heuristic did not work. Moreover, statistical hypothesis and comparison tests further validated the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Researchers from the University of Toronto have developed a digital platform called Genetics Navigator to address the increasing demand for genetic services and the strain it puts on the standard model of genetic healthcare. The platform aims to support the collection of medical and family history, provide pregenetic and postgenetic counseling, and deliver genetic testing results. The effectiveness of Genetics Navigator will be evaluated through a randomized controlled trial involving 130 participants in Ontario genetics clinics. The primary outcome of the trial is participant distress two weeks after receiving test results, and secondary outcomes include knowledge, decisional conflict, anxiety, empowerment, quality of life, satisfaction, acceptability, digital health literacy, and health resource use. The results of the trial will be disseminated through various channels, including conferences and peer-reviewed journals. [Extracted from the article]

The article offers information on a recent report from the University of Novi Sad, Serbia, detailing the fundamentals of hypothesis testing and statistical test selection in clinical research. Topics discussed include the steps involved in hypothesis testing; the importance of choosing appropriate statistical tests; and the role of key statistical values like the p-value in interpreting study results.

A research study conducted by McMaster University in Canada explores the disparity between statistical and clinical significance in published clinical trials. The study highlights the ongoing criticism of the frequentist paradigm of null hypothesis statistics testing and its reliance on the p-value and statistical significance. The researchers aim to understand the extent of the problem and identify factors associated with discrepant results in these studies. The study will analyze a sample of 500 published randomized controlled trials between 2018 and 2022 to assess the clinical importance of trial results and determine the disparity between statistical and clinical significance. The research has received ethical approval and will be disseminated as a thesis, conference abstract, and peer-reviewed manuscript. [Extracted from the article]

[Display omitted] • PCTBagging: new classifier generated by fusing consolidated trees and bagging. • Allows trade-off between discriminating capacity and interpretability. • Consolidation percentage (CP): from consolidated tree (100%) to Bagging (0%). • The lower the CP, the lower the interpretability, but the higher the accuracy. • Experiments: 96 datasets, 4 resampling options and state of art significance tests. The use of decision trees considerably improves the discriminating capacity of ensemble classifiers. However, this process results in the classifiers no longer being interpretable, although comprehensibility is a desired trait of decision trees. Consolidation (consolidated tree construction algorithm, CTC) was introduced to improve the discriminating capacity of decision trees, whereby a set of samples is used to build the consolidated tree without sacrificing transparency. In this work, PCTBagging is presented as a hybrid approach between bagging and a consolidated tree such that part of the comprehensibility of the consolidated tree is maintained while also improving the discriminating capacity. The consolidated tree is first developed up to a certain point and then typical bagging is performed for each sample. The part of the consolidated tree to be initially developed is configured by setting a consolidation percentage. In this work, 11 different consolidation percentages are considered for PCTBagging to effectively analyse the trade-off between comprehensibility and discriminating capacity. The results of PCTBagging are compared to those of bagging, CTC and C4.5, which serves as the base for all other algorithms. PCTBagging, with a low consolidation percentage, achieves a discriminating capacity similar to that of bagging while maintaining part of the interpretable structure of the consolidated tree. PCTBagging with a consolidation percentage of 100% offers the same comprehensibility as CTC, but achieves a significantly greater discriminating capacity. [ABSTRACT FROM AUTHOR]

Dynamic Bayesian networks have been well explored in the literature as discrete-time models: however, their continuous-time extensions have seen comparatively little attention. In this paper, we propose the first implementation of a constraint-based algorithm for learning the structure of continuous-time Bayesian networks. We discuss the different statistical tests and the underlying hypotheses used by our proposal to establish conditional independence. Furthermore, we analyze and discuss the computational complexity of the best and worst cases for the proposed algorithm. Finally, we validate its performance using synthetic data, and we discuss its strengths and limitations comparing it with the score-based structure learning algorithm from Nodelman et al. [23]. We find the latter to be more accurate in learning networks with binary variables, while our constraint-based approach is more accurate with variables assuming more than two values. Numerical experiments confirm that score-based and constraint-based algorithms are comparable in terms of computation time. [ABSTRACT FROM AUTHOR]

• We propose IndeD, a new preference-based method for multi-objective recommendation. • A new dominance relation concept considering the users' preferences is defined. • The new decision making process minimizes the distance to the user's preferences. • Our method beats competitive baselines in meeting individualized users' preferences. • IndED obtains the best results in the optimization of the most difficult objectives. Recommender Systems (RSs) make personalized suggestions of relevant items to users. However, the concept of relevance may involve different quality aspects (objectives), such as accuracy , novelty , and diversity. In addition, users may have their own expectations regarding what characterizes a good recommendation. More specifically, individual users may wish to prioritize the multiple objectives in different proportions based on their preferences. Previous studies on Multi-Objective (MO) recommendation do not prioritize objectives according to the individual users' preferences systematically or are biased towards a single objective as in re-ranking strategies. Moreover, traditional preference-based multi-objective solutions do not address the specificities of RSs. In this work, we propose IndED (Individualized Extreme Dominance), a new preference-based method for MO-RSs. IndED explores the concepts of Extreme Dominance and Statistical Significance Tests in order to define a new Pareto-based dominance relation that guides the optimization search considering users' preferences. We also consider a new decision making process that minimizes the distance to the individual user's preferences. Experiments show that IndED outperformed competitive baselines, obtaining results closer to the users' preferences and better balancing the objectives trade-offs. IndED is also the method that obtains the best performance regarding the most difficult objective in each considered scenario. [ABSTRACT FROM AUTHOR]

DPC (Clustering by fast search and find of Density Peaks) algorithm and its variations typically employ Euclidean distance, overlooking the diverse contributions of individual feature to similarity and subsequent clustering. To address this limitation, the standard deviation weighted distance is proposed in this paper to enhance the Euclidean distance. This weighted distance takes into account the specific contribution of each feature to the distance (similarity) between data points. By utilizing this weighted distance, the local density ρ i and distance δ i of point i are defined, thereby capturing the local pattern of point i to the fullest extent possible. Outliers are defined using this innovative weighted distance. The divide and conquer assignment strategy is proposed based on this proposed weighted distance and the semi-supervised learning and the mutual K-nearest neighbor assumption. Consequently, the SFKNN-DPC (Standard deviation weighted distance and Fuzzy weighted K-Nearest Neighbors based Density Peak Clustering) algorithm is proposed, aiming to effectively uncover the hidden clusters within a dataset. Extensive experiments conducted on benchmark datasets demonstrate the superiority of SFKNN-DPC over DPC, its variations, and other benchmark clustering algorithms. Moreover, statistical significance tests indicate that SFKNN-DPC exhibits notable differences when compared to its counterparts. • Standard deviation weighted distance is proposed to enhance the Euclidean distance. • Local density ρ i and distance δ i of point i are defined utilizing the innovative distance, so do outliers and non-outliers. • Divide and conquer assignment strategy is proposed for assigning non-outliers and outliers in turn. • An innovative density peak clustering algorithm referred to SFKNN-DPC is proposed. • Extensive experiments demonstrate that SFKNN-DPC is superior to the peers in comparison. [ABSTRACT FROM AUTHOR]

The paper investigates a hypothesis testing problem concerning the potential additional contributions of other covariates to the structural function, given the known covariates. The structural function is the conditional expectation given covariates in which the response may depend on unknown nuisance functions. It includes classic regression functions and the conditional average treatment effects as illustrative instances. Based on Neyman's orthogonality condition, the proposed distance-based test exhibits the quasi-oracle property in the sense that the nuisance function asymptotically does not influence on the limiting distributions of the test statistic under both the null and alternatives. This novel test can effectively detect the local alternatives distinct from the null at the fastest possible rate in hypothesis testing. This is particularly noteworthy given the involvement of nonparametric estimation of the conditional expectation. Numerical studies are conducted to examine the performance of the test. In the real data analysis section, the proposed tests are applied to identify significantly explanatory covariates that are associated with AIDS treatment effects, yielding noteworthy insights. [ABSTRACT FROM AUTHOR]

A machine learning algorithm is monotonic if it returns a model with better performance when trained with a larger data set. Monotonicity is essential in scenarios when a learning algorithm is working with continually collected data, as non-monotonicity may result in unstable performance and a huge waste of resources during the learning process. However, existing learning algorithms working in scenarios such as online learning, domain incremental learning and reinforcement learning hardly address the monotonicity issue. In this paper, we propose an evolutionary framework that focuses on the enforcement of monotonicity for a learning algorithm over streaming data feeds. In each iteration, training is triggered by a new collection of incoming data, which consequently creates a new generation of hypotheses, and only a portion of the generation with best performance is retained for the next round based on a novel statistical hypothesis test. We carry out experiments on DNN models with continual data feeds constructed from MNIST, CIFAR-10, SST-2 and Tiny ImageNet. The results justify that our approach can significantly increase the probability of locally monotonic updates on the generated learning curves for the trained models and outperforms the state-of-the-art methods on that purpose. [ABSTRACT FROM AUTHOR]

Facial expression recognition rarely explores complex spatiotemporal dependencies among facial regions at different scales. This paper proposes a transformer-based three-layer hierarchical architecture that incorporates multi-scale spatiotemporal aggregation for dynamic facial expression recognition. The hierarchical structure consists of bottom-to-top layers, each comprising transformer encoders with local self-attention mechanisms. These encoders gradually expand their receptive fields through hierarchical spatiotemporal aggregation, enabling the modeling of spatiotemporal context dependencies among facial regions at different scales and across consecutive frames. Consequently, the bottom-to-top layers correspond to learning the fine-grained, coarse-grained, and global facial representations. To evaluate the performance of our proposed framework, we conducted extensive experiments on four public datasets. The comparison results demonstrate that our proposed framework outperforms the state-of-the-art, with accuracies of 79.09%, 62.19%, 64.85%, and 59.79% on the RML, eNTERFACE'05, RAVDESS, and AFEW datasets, respectively. Ablation experiments, statistical significance tests, and visualization analyses indicate that the proposed framework successfully learns emotional-salient facial representations. • A transformer-based hierarchical framework for dynamic facial expression recognition. • Model dynamic dependencies between multi-scale facial regions via spatiotemporal aggregation. • Extensive experiments on four benchmarks show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The mean fluoroscopy time and estimated radiation dose were significantly reduced in patients who underwent GJ tube placement with a post-pyloric DHT in position compared with those without (7.08 min vs. 11.02 min, P = 0.004; 123.12 mGy vs. 255.19 mGy, P = 0.015, respectively). Of the 71 GJ tube placements included for analysis, 12 patients underwent placement with a post-pyloric DHT in position, and 59 patients underwent placement without a post-pyloric DHT in position. Keywords: Gastroenterology; Gastrojejunostomy; Health and Medicine EN Gastroenterology Gastrojejunostomy Health and Medicine 551 551 1 09/19/23 20230919 NES 230919 2023 SEP 18 (NewsRx) -- By a News Reporter-Staff News Editor at Gastroenterology Week -- New research on gastroenterology is the subject of a new report. [Extracted from the article]

Many dynamic processes in the real world can be modeled as time series, so time series prediction is significant for social and economic development. The inherent non-stationarity of time series obtained from actual projects may make it difficult to predict accurately. To alleviate this problem, in this paper, a Double Incremental Learning algorithm via Adaptive Ensemble, termed as AE-DIL for short, is proposed for non-stationary time series prediction. AE-DIL provides a general online prediction framework consisting of two modules. The first detects changes based on the statistical hypothesis test and self-adaptive sliding window technology. The second updates the prediction model based on double incremental learning and adaptive ensemble learning. The effectiveness of the proposed algorithm is empirically underpinned by the experiments conducted on seven benchmark time series datasets, compared with several baselines and state-of-the-art models. [ABSTRACT FROM AUTHOR]

Keywords for this news article include: Chungbuk, South Korea, Asia, Mathematics, Bayesian Statistic, Food Research, Korea National University of Transportation. Keywords: Chungbuk; South Korea; Asia; Mathematics; Bayesian Statistic; Food Research EN Chungbuk South Korea Asia Mathematics Bayesian Statistic Food Research 265 265 1 07/03/23 20230706 NES 230706 2023 JUL 6 (NewsRx) -- By a News Reporter-Staff News Editor at Food Weekly News -- Investigators discuss new findings in Mathematics. [Extracted from the article]

Keywords: Chronic Liver Disease; Digestive System Diseases and Conditions; Health and Medicine; Hepatology; Liver Diseases and Conditions EN Chronic Liver Disease Digestive System Diseases and Conditions Health and Medicine Hepatology Liver Diseases and Conditions 143 143 1 06/19/23 20230620 NES 230620 2023 JUN 19 (NewsRx) -- By a News Reporter-Staff News Editor at Gastroenterology Week -- Data detailed on chronic liver disease have been presented. Chronic Liver Disease, Digestive System Diseases and Conditions, Health and Medicine, Hepatology, Liver Diseases and Conditions. [Extracted from the article]

In the age of big data, data integration is a critical step especially in the understanding of how diverse data types work together and work separately. Among data integration methods, the Angle-Based Joint and Individual Variation Explained (AJIVE) approach is particularly attractive because it not only studies joint behavior but also individual behavior. Typically AJIVE scores indicate important relationships between data objects, such as clusters. An important challenge is understanding which features, i.e. variables, are associated with those relationships. This challenge is addressed by the proposal of a hypothesis test for assessing statistical significance of features. The new test is inspired by the related jackstraw method developed for Principal Component Analysis. We use a high-dimensional multi-genomic cancer data set as our strong motivation and deep illustration of the methodology. [ABSTRACT FROM AUTHOR]

In materials science and many other application domains, 3D information can often only be obtained by extrapolating from 2D slices. In topological data analysis, persistence vineyards have emerged as a powerful tool to take into account topological features stretching over several slices. It is illustrated how persistence vineyards can be used to design rigorous statistical hypothesis tests for 3D microstructure models based on data from 2D slices. More precisely, by establishing the asymptotic normality of suitable longitudinal and cross-sectional summary statistics, goodness-of-fit tests that become asymptotically exact in large sampling windows are devised. The testing methodology is illustrated through a detailed simulation study and a prototypical example from materials science is provided. [ABSTRACT FROM AUTHOR]

The Lorenz regression procedure quantifies the inequality of a response explained by a set of covariates. Formally, it gives a weight to each covariate to maximize the concentration index between the response and a weighted average of the covariates. The obtained index is called the explained Gini coefficient. Unlike methods based on decompositions of inequality measures, the procedure does not assume a linear relationship between the response and the covariates. Inference can be performed by noticing a similarity with the monotone rank estimator, introduced in the context of the single-index model. A continuity correction is presented in the presence of discrete covariates. The Lorenz- R 2 is a goodness-of-fit measure evaluating the proportion of explained inequality and is used to build a test of joint significance of several covariates. Monte-Carlo simulations and a real-data example are presented. [ABSTRACT FROM AUTHOR]

• Bayesian hypothesis tests present an alternative to NHST and p-values. • By now, calibration of the power and error rates of Bayesian tests is challenging. • This paper demonstrates how to balance errors and power for nonparametric Bayesian two-sample tests. • The dependence of the prior modelling and influence of sample size is investigated. • Bayesian nonparametric two-sample tests can be calibrated via the presented results. Hypothesis testing is a central statistical method in the biomedical sciences. The ongoing debate about the concept of statistical significance and the reliability of null hypothesis significance tests (NHST) and p-values has brought the advent of various Bayesian hypothesis tests as possible alternatives, which often employ the Bayes factor. However, careful calibration of the prior parameters is necessary for the type I error rates or power of these alternatives to be any better. Also, the availability of various Bayesian tests for the same statistical problem leads to the question which test to choose based on which criteria. Recently proposed Bayesian nonparametric two-sample tests are analyzed with regard to their type I error rates and power to detect an effect. Results show that approaches vary substantially in their ability to control the type I and II errors, and it is shown how to select the prior parameters to balance power and type I error control. This allows for prior elicitation and power analyses based on objective criteria like type I and II error rates even when conducting a Bayesian nonparametric two-sample test. Also, it is shown that existing nonparametric Bayesian two-sample tests are adequate only to test for location-shifts. Together, the results provide guidance how to perform a nonparametric Bayesian two-sample test while simultaneously improving the reliability of research. [ABSTRACT FROM AUTHOR]