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AbstractIn genomic research, identifying the exon regions in eukaryotes is the most cumbersome task. This article introduces a new promising model-independent method based on short-time discrete Fourier transform (ST-DFT) and fine-tuned variational mode decomposition (FTVMD) for identifying exon regions. The proposed method uses the N/3 periodicity property of the eukaryotic genes to detect the exon regions using the ST-DFT. However, background noise is present in the spectrum of ST-DFT since the sliding rectangular window produces spectral leakage. To overcome this, FTVMD is proposed in this work. VMD is more resilient to noise and sampling errors than other decomposition techniques because it utilizes the generalization of the Wiener filter into several adaptive bands. The performance of VMD is affected due to the improper selection of the penalty factor (α), and the number of modes (K). Therefore, in fine-tuned VMD, the parameters of VMD (K and α) are optimized by maximum kurtosis value. The main objective of this article is to enhance the accuracy in the identification of exon regions in a DNA sequence. At last, a comparative study demonstrates that the proposed technique is superior to its counterparts. [ABSTRACT FROM AUTHOR]

The echo signal of ultrasonic internal inspection is highly sensitive to the incident deflection angle between the probe and the measured surface of the pipeline, which is the key factor affecting the accuracy of ultrasonic inspection. In practical engineering, the echo signal of ultrasonic inspection is easy to be interfered by the external environment, which makes it difficult to identify the deflection angle. The data of defect echo signal under different circumferential declination were collected by the pipeline ultrasonic internal inspection system, and the characteristics of signal variation in time domain under different circumferential declination were analysed. The results show that the existence of circumferential declination reduced the absolute value of the overall amplitude of ultrasonic echo signal. A signal processing method based on amplitude reduction is proposed to reduce the sampling random error. Comparative analysis shows that the circumferential declination can be identified based on amplitude reduction method, and the accuracy of ultrasonic recognition can be improved. By extracting the characteristic value of waveform, the empirical formula is obtained. Within 2°, the maximum deviation is 0.137°. The research results have certain guiding significance for the identification of circumferential declination in practical engineering. [ABSTRACT FROM AUTHOR]

Bootstrap is a widely applicable computational statistical method. The focus of this paper lies in developing a bootstrap method for linear mixed models under homoscedasticity violation (heteroscedasticity) in variance of errors. We assume that the form of heteroscedasticity is unknown. To generate bootstrap response data as close as possible to the actual response data, we transform the marginal residuals to ensure that the variance of the modified marginal residuals in bootstrap samples is an unbiased estimator for the variance of the response variable in the linear mixed models. The consistency is proved, implying that the parameter estimators by the proposed method are asymptotically unbiased. The simulations are conducted with varying error terms and sample sizes to show improvement of the proposed method in parameter estimation. The simulation results are compared to demonstrate that the proposed method outperforms the other considered approaches including the wild method in the heteroscedastic linear mixed models under small samples and performs competitively with the parametric method and both methods outperform the other considered methods in homoscedastic linear mixed models. The proposed bootstrap method is applied to squid data for illustration of its effectiveness. [ABSTRACT FROM AUTHOR]

Maintaining high levels of process quality is crucial to the competitiveness of manufacturing firms in today's increasingly global marketplace. To ensure the quality of manufactured products meets customer needs, process capability indices (PCIs) are widely used to analyze the process performance of various processing characteristics. Products characterise by processing characteristics of both unilateral and bilateral specifications are common in the current sales market. Manufacturing firms must often adopt multiple PCIs to analyze the process performance of a single product, which is inefficient in practical applications and management. Yield-based index C p k is not subject to this limitation. For this reason, we employed C p k to evaluate process performance and the effectiveness of improvement measures. In practice, C p k is estimated from samples, which means that misjudgment may occur in the assessment of process performance and improvement effectiveness due to sampling errors. We therefore derived the 100 (1 − α) % confidence interval of C p k and, based on the producer's perspective, used the upper confidence limit to evaluate improvement effectiveness. To lower the risk of misjudgment and increase the reliability of improvement effectiveness in the case of data uncertainty, this paper further proposes fuzzy estimation using the right-sided confidence interval of C p k and develops the fuzzy judgement model. [ABSTRACT FROM AUTHOR]

With clustered data, such as where students are nested within schools or employees are nested within organizations, it is often of interest to estimate and compare associations among variables separately for each level. While researchers routinely estimate between-cluster effects using the sample cluster means of a predictor, previous research has shown that such practice leads to biased estimates of coefficients at the between level, and recent research has recommended the use of latent cluster means with the multilevel structural equation modeling framework. However, the latent cluster mean approach may not always be the best choice as it (a) relies on the assumption that the population cluster sizes are close to infinite, (b) requires a relatively large number of clusters, and (c) is currently only implemented in specialized software such as Mplus. In this paper, we show how using empirical Bayes estimates of the cluster means can also lead to consistent estimates of between-level coefficients, and illustrate how the empirical Bayes estimate can incorporate finite population corrections when information on population cluster sizes is available. Through a series of Monte Carlo simulation studies, we show that the empirical Bayes cluster-mean approach performs similarly to the latent cluster mean approach for estimating the between-cluster coefficients in most conditions when the infinite-population assumption holds, and applying the finite population correction provides reasonable point and interval estimates when the population is finite. The performance of EBM can be further improved with restricted maximum likelihood estimation and likelihood-based confidence intervals. We also provide an R function that implements the empirical Bayes cluster-mean approach, and illustrate it using data from the classic High School and Beyond Study. [ABSTRACT FROM AUTHOR]

Epidemiologic screening programs often make use of tests with small, but nonzero probabilities of misdiagnosis. In this article, we assume the target population is finite with a fixed number of true cases, and that we apply an imperfect test with known sensitivity and specificity to a sample of individuals from the population. In this setting, we propose an enhanced inferential approach for use in conjunction with sampling-based bias-corrected prevalence estimation. While ignoring the finite nature of the population can yield markedly conservative estimates, direct application of a standard finite population correction (FPC) conversely leads to underestimation of variance. We uncover a way to leverage the typical FPC indirectly toward valid statistical inference. In particular, we derive a readily estimable extra variance component induced by misclassification in this specific but arguably common diagnostic testing scenario. Our approach yields a standard error estimate that properly captures the sampling variability of the usual bias-corrected maximum likelihood estimator of disease prevalence. Finally, we develop an adapted Bayesian credible interval for the true prevalence that offers improved frequentist properties (i.e., coverage and width) relative to a Wald-type confidence interval. We report the simulation results to demonstrate the enhanced performance of the proposed inferential methods. [ABSTRACT FROM AUTHOR]

The second version of Kaiser's Measure of Sampling Adequacy (MSA2) has been widely applied to assess the factorability of data in psychological research. The MSA2 is developed in the population and little is known about its behavior in finite samples. If estimated MSA2s are biased due to sampling errors, misleading inferences on the factorability of data are likely to occur. This study investigates the effect of sampling error on MSA2 estimation by systematically examining the accuracy and fluctuations of MSA2 estimates with simulated continuous and ordered categorical data. Features manipulated included the number of factors, number of variables per factor, factor loading, inter-factor correlations, sample size, number of response categories, skewness of variables, and types of correlation analyzed, Pearson and polychoric correlations. Results revealed that the MSA2s were underestimated due to the effect of sampling error. Severely biased MSA2 occurred when analyzing a large number of weakly correlated variables with insufficient participants. The underestimation of MSA2 became worse with categorized data. Polychoric correlations yielded slightly more accurate but relatively unstable MSA2 estimates compared to Pearson correlations. In practice, researchers need to bear in mind the downward bias of MSA2 estimates and interpret the value of sample MSA2s with caution. [ABSTRACT FROM AUTHOR]

In today's steady development of e-commerce, improving enterprise competitiveness is a major challenge that e-commerce enterprises need to face. There are problems with incomplete and inaccurate evaluation systems in commonly used competitiveness evaluation systems. To address these concerns, the study establishes a model for evaluating business-to-business relationships by analyzing indicators of competitiveness. It also examines the algorithm process and neural network structure and incorporates them into the evaluation model. The experiment shows that through training the back propagation neural network, it is discovered that the mean square error of the samples is 9.9869e-06 when the training frequency reaches 3200, resulting in the completion of convergence of the back-propagation neural network. The utilization of back propagation neural networks to enhance the evaluation model of enterprise competitiveness has yielded positive outcomes. Suggestions have been proposed to improve the competitiveness of diverse e-commerce enterprises. This study can furnish enterprise managers with decision support, enhancing enterprise competitiveness and enabling sustainable development. [ABSTRACT FROM AUTHOR]

Over the years, acceptance sampling plans have been crucial to quality assurance in manufacturing. Sample plans are designed using operating characteristic curve conditions to safeguard producers and customers. We propose a conditional probability-based Bayesian generalized multiple-dependent state sampling technique in this paper. The technique relies on Gamma-Poisson distribution. Other performance indicators and acceptance probability are calculated. Also, the new plan's operational method is discussed. The proposed technique is also compared to current attribute sampling schemes for efficacy. Optimal plan parameters for the plan's economic structure are also generated, adding managerial insights to the suggested plan. The entire cost study showed that the suggested plan is cheaper than existing sample plans under identical conditions. To account for inspection flaws, the plan is adjusted. We examine how Type I and Type II errors affect sampling plan outcomes. The plan is demonstrated with numerical examples and a data-driven application. [ABSTRACT FROM AUTHOR]

Detrital zircon (DZ) geochronology is based on a multistage, hierarchical sampling process in which sampling errors (statistical variance) and non-sampling errors (sampling bias) are endemic. Most of these errors are related to 1) initial collection of a non-random grab sample (typically from outcrop); 2) processing the grab sample to obtain a non-random grain-size sample of restricted grain size(s) (effectively a processed population); 3) randomly and/or non-randomly selecting DZs (often based on their size, shape, and colour) from the grain-size sample to obtain a hand-picked sample (effectively an analysed population); and 4) randomly and/or non-randomly radiometrically dating only certain DZs (due to limitations of instrument spot size and grain imperfections) from the hand-picked sample to obtain an analysed sample. Claims that hand-picked samples and analysed samples are randomly chosen are questionable, since these samples likely represent haphazard sampling attempting to emulate randomness. Non-randomness has major implications for calculations that determine sample size and inter-sample 'sameness' comparison, as both techniques generally depend on randomly chosen samples. The use of inter-sample comparison is particularly troublesome in that sameness is determined for the analysed sample, which is not representative of the initial grab sample. Yet more bias occurs with a 'leap of inference', the generalization of DZ ages from the analysed sample to the target population (the strata of interest) or to a general population (DZs derived from ultimate and proximate sources). Sampling bias can be reduced by unambiguously defining the sampled population (the population that inferential statistics apply), selecting representative samples (if possible, randomization should be used at every stage in the sampling process), and preserving the representativeness of subsamples by using correct sampling devices such as a microsplitter. It may be timely for standardized sampling, interpreting, and reporting guidelines so that detrital geochronology data are scientifically meaningful and replicable. [ABSTRACT FROM AUTHOR]

The problem of domain generalization is to learn, given data from different source distributions, a model that can be expected to generalize well on new target distributions which are only seen through unlabeled samples. In this paper, we study domain generalization as a problem of functional regression. Our concept leads to a new algorithm for learning a linear operator from marginal distributions of inputs to the corresponding conditional distributions of outputs given inputs. Our algorithm allows a source distribution-dependent construction of reproducing kernel Hilbert spaces for prediction, and, satisfies finite sample error bounds for the idealized risk. Numerical implementations and source code are available1. [ABSTRACT FROM AUTHOR]

Non-response situations are inevitable in sample survey work, and this is also true in dual sampling frame. In view of this, this study proposes alternative imputation techniques to solve the problem of missing data and further suggests an estimate of the population total in dual sampling frame. The properties of the suggested estimator are analyzed, and its empirical results are compared to the entire response situation. Based on the results obtained, appropriate suggestions have been proposed to survey practitioners and researchers for their practical applications. [ABSTRACT FROM AUTHOR]

Objectives: Benign category of Bethesda classification is generally well known to carry a false-negative rate of 0--3%. The current study was designed to investigate the rate of falsenegative cytology in patients who underwent thyroidectomy for presumably benign thyroid diseases. Predictive risk factors for false results and malignancy were evaluated along with cytology--histology discrepant cases. Materials and methods: Females who underwent thyroidectomy between May 2014 and December 2022 were included. Demographics, ultrasound (US) features, fine-needle aspiration (FNA) diagnosis, surgical indications and outcomes, final histology reports, risk factors, and malignancy rate were recorded. Cytology--histology discrepant cases were further evaluated for interpretation errors and risk factors. Statistical analyses were performed using Fisher's exact and Mann--Whitney U tests. Results: Of 581 women with a benign thyroid disease who underwent thyroidectomy, 91 was diagnosed as incidental carcinoma (15.6%) and most was T1a (4.9 ± 2.7 mm, 95.6%). Final histology reports revealed mostly papillary carcinoma (93.4%). Predictors of malignancy such as age, family history, previous radiation exposure, and iodine-deficient diet did not help in risk stratification (p > 0.05, for each). However, FNA taken during pregnancy was determined as a risk factor (n = 7, 7.6%, p < 0.05) since it may cause a delay in diagnosis. Cytology--histology discrepant cases were seen to be mostly due to sampling errors (45%, p < 0.05), followed by misinterpretations (37.3%, p < 0.05). There was no reason for discrepancy in 17.5%, and this was linked to inherent nature of thyroid nodule with overlapping cytologic features. Best identifiable risk factor for misinterpretation was pregnancy as well (n = 5, 14.7%, p < 0.05). Conclusions: Risk of malignancy in a presumably benign thyroid disease should not be ignored. Radiology--cytology correlation by an experienced dedicated team may help in decreasing sampling errors. Physiologic changes caused by pregnancy may shade malignant transformation in thyrocytes, and it would be appropriate to be cautious about benign FNA taken during this period. [ABSTRACT FROM AUTHOR]

This article proposes a variable-sample method for the computation of stochastic stable Nash equilibrium, in which the objective functions are approximated, in each iteration, by the sample average approximation with different sample sizes. We start by investigating the contraction mapping properties under the variable-sample framework. Under some moderate conditions, it is shown that the accumulation points attained from the algorithm satisfy the first-order equilibrium conditions with probability one. Moreover, we use the asymptotic unbiasedness condition to prove the convergence of the accumulation points of the algorithm into the set of fixed points and prove the finite termination property of the algorithm. We also verify that the algorithm converges to the equilibrium even if the optimization problems in each iteration are solved inexactly. In the numerical tests, we comparatively analyze the accuracy error and the precision error of the estimators with different sample size schedules with respect to the sampling loads and the computational times. The results validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The accurate measurement of PM2.5 and its inorganic matters (IMs) is crucial for compliance monitoring and understanding particle formation. However, semi-volatile IMs (SVIMs) like NH4+, NO3-, and Cl- tend to evaporate from particles, causing sampling artifacts. The evaporation loss occurs due to many factors making the quantitative prediction difficult. This study aimed to investigate the evaporation loss of SVIMs in PM2.5 under different sampling conditions. In the field tests, when a normal single Teflon filter (STF) sampler, which is like a Federal Reference Method (FRM) sampler, was used to sample PM2.5 at ambient conditions, a significant SVIM evaporation loss was observed, resulting in negative biases for total IMs (-25.68 ± 3.25%) and PM2.5 concentrations (-9.87 ± 4.27%). But if PM2.5 was sampled by a chilled Teflon filter sampler (CTF) at 4 °C following aerosol dehumidification so that relative humidity (RH) was controlled to within the 10-20% range (RHd), evaporation loss was minimized with a bias of <±10% for both total IMs and PM2.5based on the reference data. When RHd is below 10%, both IMs and PM2.5 are under-measured, but only PM2.5 is over-measured when RHd is >20%. A model considering predictable saturation ratios for NH4+, NO3-, and Cl- under various pressure drop, temperature and RH conditions was developed to predict accurately the actual concentrations of PM2.5 and its SVIMs for the STF. Additionally, the ISORROPIA-II model predicted SVIMs effectively for the CTF. In summary, using the CTF at optimized sampling conditions can achieve accurate measurement of both SVIMs and PM2.5 concentrations simultaneously. [ABSTRACT FROM AUTHOR]

We study the problem of detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models, whose transition matrices exhibit low rank plus sparse structure. We first address the problem of detecting a single change point using an exhaustive search algorithm and establish a finite sample error bound for its accuracy. Next, we extend the results to the case of multiple change points that can grow as a function of the sample size. Their detection is based on a two-step algorithm, wherein the first step, an exhaustive search for a candidate change point is employed for overlapping windows, and subsequently a backward elimination procedure is used to screen out redundant candidates. The two-step strategy yields consistent estimates of the number and the locations of the change points. To reduce computation cost, we also investigate conditions under which a surrogate VAR model with a weakly sparse transition matrix can accurately estimate the change points and their locations for data generated by the original model. This work also addresses and resolves a number of novel technical challenges posed by the nature of the VAR models under consideration. The effectiveness of the proposed algorithms and methodology is illustrated on both synthetic and two real datasets. for this article are available online. [ABSTRACT FROM AUTHOR]

Log-normal, Weibull, and log-logistic distributions are widely used in modeling nonnegative skewed data. We develop sequential methodologies to discriminate between any two of these three distributions as well as to discriminate among these three distributions. These methods are extended to discriminate Mð 2Þ distributions from location-scale, log-location-scale and regular families of distributions. Discriminating three or more distributions having similar shapes often requires large sample size. Sequential procedures allow early stopping which in turn reduce the sample size needed for discrimination. Proposed methods yield high probabilities of correct selection that are shown to converge to 1 asymptotically. Asymptotic behavior of expected sample size and error probabilities are studied as stopping boundaries tend to infinity. Extensive simulation study validates finite sample performances of the proposed procedures requiring significantly fewer samples on average. These methods are applied to three benchmark datasets on cancer trials and are shown to select the correct model with high probability [ABSTRACT FROM AUTHOR]

A multi-disciplinary approach to estimate discharge is proposed that is ideal for field conditions and also addresses the shortcomings of rating curve method. First, the discharge is defined as a function of a section's hydraulic characteristics, and it is assumed that an approximate convective-diffusive wave governs the flood wave that passes through the section under an unsteady flow situation. Only the stage data are needed by the generated model to predict discharge. Second, an IoT (Internet of Things)-based gauging station was established to collect stage data. The near-real-time stage data is captured using an algorithm designed to minimize the sampling error. Therefore, the multidisciplinary solution has addressed important issues for researchers and field engineers. Additionally, it may be used to estimate discharge in near-real-time at the chosen temporal frequency. The suitability and accuracy of the model were tested at a USGS (United States Geological Survey) gauging station considering three flooding events. The applicability evaluated using six evaluation criteria indicated an accuracy of 98.075% on average. Finally, it was employed over an un-gauged river site at Om-Chhu to estimate discharge in near-near-real-time at a temporal frequency of 5-min intervals. [ABSTRACT FROM AUTHOR]

"Statistical Methods in Health Disparity Research" by J. Sunil Rao is a comprehensive book that explores statistical methods for estimating health disparities. The book covers various approaches, including non-model and linear-based models, as well as the application of machine learning. It emphasizes the importance of validating findings and discusses the advantages and limitations of each method. The book is divided into seven chapters, which cover topics such as basic concepts of health disparities, estimation of health disparities, domain-specific estimates, causality, moderation, and mediation, machine learning-based approaches, health disparity estimation under a precision medicine paradigm, and extended topics. While the book has some structural inconsistencies, it offers a diverse range of topics and provides in-depth explanations of complex concepts. It is recommended for students, professionals in public health or health policy, researchers, and practitioners working with vulnerable populations. [Extracted from the article]

Convex clustering has recently garnered increasing interest due to its attractive theoretical and computational properties, but its merits become limited in the face of high-dimensional data. In such settings, pairwise affinity terms that rely on k-nearest neighbors become poorly specified and Euclidean measures of fit provide weaker discriminating power. To surmount these issues, we propose to modify the convex clustering objective so that feature weights are optimized jointly with the centroids. The resulting problem becomes biconvex, and as such remains well-behaved statistically and algorithmically. In particular, we derive a fast algorithm with closed form updates and convergence guarantees, and establish finite-sample bounds on its prediction error. Under interpretable regularity conditions, the error bound analysis implies consistency of the proposed estimator. Biconvex clustering performs feature selection throughout the clustering task: as the learned weights change the effective feature representation, pairwise affinities can be updated adaptively across iterations rather than precomputed within a dubious feature space. We validate the contributions on real and simulated data, showing that our method effectively addresses the challenges of dimensionality while reducing dependence on carefully tuned heuristics typical of existing approaches. for this article are available online. [ABSTRACT FROM AUTHOR]

With the emergence of precision medicine, estimating optimal individualized decision rules (IDRs) has attracted tremendous attention in many scientific areas. Most existing literature has focused on finding optimal IDRs that can maximize the expected outcome for each individual. Motivated by complex individualized decision making procedures and the popular conditional value at risk (CVaR) measure, we propose a new robust criterion to estimate optimal IDRs in order to control the average lower tail of the individuals' outcomes. In addition to improving the individualized expected outcome, our proposed criterion takes risks into consideration, and thus the resulting IDRs can prevent adverse events. The optimal IDR under our criterion can be interpreted as the decision rule that maximizes the "worst-case" scenario of the individualized outcome when the underlying distribution is perturbed within a constrained set. An efficient non-convex optimization algorithm is proposed with convergence guarantees. We investigate theoretical properties for our estimated optimal IDRs under the proposed criterion such as consistency and finite sample error bounds. Simulation studies and a real data application are used to further demonstrate the robust performance of our methods. Several extensions of the proposed method are also discussed. for this article are available online. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of the mean square error control chart in detecting small to moderate shifts, an improved variable parameter mean square error (VPt MSE) control chart is proposed, which uses three different sampling intervals, two different sample sizes and two different control limits. Markov chain method is used to calculate the average time to signal and average number of observations to signal. The performance of the adaptive chart was compared with the variable sample size and sampling interval (VSSI), the improved variable sample size and sampling interval (VSSIt), the variable parameter (VP), the exponentially weighted moving average (EWMA), and the variable sampling interval exponentially weighted moving average (VSI EWMA) mean square error control chart. Numerical results show that the VPt MSE chart surpasses the other charts in detecting small to moderate shifts. At the same time, we also improve the VPt MSE chart, which greatly improves the statistical performance. An example is provided to illustrate the implementation of the VPt MSE chart. [ABSTRACT FROM AUTHOR]

This paper deals with testing traffic intensity, which is the most important parameter of the M / M / 1 queue system. The Wald-type sequential probability ratio test (SPRT) is performed to determine traffic intensity by taking advantage of the fact that the total number of customers arriving up to the kth service period, including the kth service period, has a negative binomial distribution. Acceptance and rejection limits obtained from the SPRT are arranged as a multiple sampling plan. This plan is applied with a finite Markov chain. Then, the Operating Characteristic (OC) function and the Average Sample Number (ASN) are obtained precisely. Obtaining the standard errors of the sample number compared to the studies on SPRT applications is the novelty brought by this paper. The sequential analysis method based on the finite Markov chain is also applied to a numerical sample. [ABSTRACT FROM AUTHOR]

The current study investigated the effects of the concrete-representational-abstract integrated sequence (CRA-I) for teaching fifth grade students receiving tier two interventions within a multi-tiered system of support (MTSS). The research questions addressed the following: the extent to which students' rational number skills changed as a result receiving CRA-I, the extent to which students receiving CRA-I decreased their fraction estimation error, the extent to which students receiving CRA-I and their teachers perceived it to be socially valid, and the extent to which students who received CRA-I intervention maintained their performance two months after instruction. Using a quasi-experimental design with randomly assigned matched pairs, researchers taught 11 students using CRA-I and compared their performance to 11 of their peers in a comparison group who waited to receive intervention. The researchers found a significant difference in performance favoring the CRA-I group. The implications of the results will be discussed. [ABSTRACT FROM AUTHOR]

This article describes three methods for carrying out nonasymptotic inference on partially identified parameters that are solutions to a class of optimization problems. Applications in which the optimization problems arise include estimation under shape restrictions, estimation of models of discrete games, and estimation based on grouped data. The partially identified parameters are characterized by restrictions that involve the unknown population means of observed random variables in addition to structural parameters. Inference consists of finding confidence intervals for functions of the structural parameters. Our theory provides finite-sample lower bounds on the coverage probabilities of the confidence intervals under three sets of assumptions of increasing strength. With the moderate sample sizes found in most economics applications, the bounds become tighter as the assumptions strengthen. We discuss estimation of population parameters that the bounds depend on and contrast our methods with alternative methods for obtaining confidence intervals for partially identified parameters. The results of Monte Carlo experiments and empirical examples illustrate the usefulness of our method. [ABSTRACT FROM AUTHOR]

The task of this article is to harness the wisdom of a crowd without calibration. We assume each expert to form predictions by linearly combining various information cues inspired by the lens model, and use Gaussian process to account for sampling and judgmental errors in quantile judgments. Without knowing the experts' observed information cues, we develop a three-step estimation algorithm to factor quantile judgments into "variable profiles" (latent cues underlying each variable of interest) and "expert profiles" (each expert's weights over these cues). We can inquire about expert similarity using their weights of the latent cues, which preserve the same clustering results as the actual weights of the observed cues up to a full-rank linear transform. We can then depict the diversity and dependency among experts explicitly and retain a subcrowd by picking delegates from each subgroup of experts based on the estimated weights. Simulation and case studies demonstrate that a subcrowd selected this way can represent the entire expert panel well. [ABSTRACT FROM AUTHOR]

Classical randomized experiments, equipped with randomization-based inference, provide assumption-free inference for treatment effects. They have been the gold standard for drawing causal inference and provide excellent internal validity. However, they have also been criticized for questionable external validity, in the sense that the conclusion may not generalize well to a larger population. The randomized survey experiment is a design tool that can help mitigate this concern, by randomly selecting the experimental units from the target population of interest. However, as pointed out by Morgan and Rubin, chance imbalances often exist in covariate distributions between different treatment groups even under completely randomized experiments. Not surprisingly, such covariate imbalances also occur in randomized survey experiments. Furthermore, the covariate imbalances happen not only between different treatment groups, but also between the sampled experimental units and the overall population of interest. In this article, we propose a two-stage rerandomization design that can actively avoid undesirable covariate imbalances at both the sampling and treatment assignment stages. We further develop asymptotic theory for rerandomized survey experiments, demonstrating that rerandomization provides better covariate balance, more precise treatment effect estimators, and shorter large-sample confidence intervals. We also propose covariate adjustment to deal with remaining covariate imbalances after rerandomization, showing that it can further improve both the sampling and estimated precision. Our work allows general relationship among covariates at the sampling, treatment assignment and analysis stages, and generalizes both rerandomization in classical randomized experiments and rejective sampling in survey sampling. [ABSTRACT FROM AUTHOR]

Point process modeling is gaining increasing attention, as point process type data are emerging in a large variety of scientific applications. In this article, motivated by a neuronal spike trains study, we propose a novel point process regression model, where both the response and the predictor can be a high-dimensional point process. We model the predictor effects through the conditional intensities using a set of basis transferring functions in a convolutional fashion. We organize the corresponding transferring coefficients in the form of a three-way tensor, then impose the low-rank, sparsity, and subgroup structures on this coefficient tensor. These structures help reduce the dimensionality, integrate information across different individual processes, and facilitate the interpretation. We develop a highly scalable optimization algorithm for parameter estimation. We derive the large sample error bound for the recovered coefficient tensor, and establish the subgroup identification consistency, while allowing the dimension of the multivariate point process to diverge. We demonstrate the efficacy of our method through both simulations and a cross-area neuronal spike trains analysis in a sensory cortex study. [ABSTRACT FROM AUTHOR]

Promptly development of new products can be achieved through quality function deployment (QFD) process, which is critical to companies' survival. Since the multi-criteria decision-making problem involved in QFD, a novel method integrating cloud model and grey relational analysis is put forward in this paper. Taking into account the subjectivity and ambiguity in linguistic evaluations, some scholars utilise fuzzy theory, rough theory, interval-valued fuzzy-rough sets and MCDM methods to improve traditional QFD. However, much priori information requirements, inability to handle subjectivity and randomness, and lack of mechanism to overcome small sample size problem are some inevitable drawbacks in these methods. To solve these deficiencies, a hybrid methodology is proposed in this paper, integrating the fortes of cloud model in processing ambiguity and randomness, and the merits of grey relational analysis in overcoming small sample size error as well as revealing the inner correlations. The comparative analysis of different approaches as well as the sensitivity analysis of criteria weights is implemented to prove the stability of the novel method. The results obtained in this paper shows that the proposed method can be a practical tool for improving the efficiency and accuracy of traditional QFD in reality management. [ABSTRACT FROM AUTHOR]

The purpose of the present study was to examine the mediating role of self-compassion in the relationship between guilt–/shame-proneness and self-forgiveness. The study is based on the Hall and Fincham's self-forgiveness model which indicated guilt and shame proneness as emotional predictors of self-forgiveness. The sample consisted of 300 participants. They completed online self-report questionnaires related to shame and guilt-proneness (TOSCA-3), self-compassion (Self-Compassion Scale) and self-forgiveness (State Self-Forgiveness Scale). The multiple mediation models were developed to assess the extent to which selfcompassion mediates the relationship between shame- and guilt –proneness and selfforgiveness. The results showed that self-compassion mediated the link between shame-proneness and self-forgiveness (Total indirect effect B=-.24 CI95% [-.331 -.164]), shame-proneness and self-forgiving feeling and action (Total indirect effect B=-.15 CI95% [-.195 -.101], and shame-proneness and self-forgiving beliefs(Total indirect effect B=-.10 CI95% [-.149 -.051]). Shame activated during a transgression leads to less understanding, forbearance and compassion toward oneself. This attitude inhibits self-forgiveness. [ABSTRACT FROM AUTHOR]

Electrocardiographic imaging (ECGI) presents a clinical opportunity to noninvasively understand the sources of arrhythmias for individual patients. To help increase the effectiveness of ECGI, we provide new ways to visualise associated measurement and modelling errors. In this paper, we study source localisation uncertainty in two steps: First, we perform Monte Carlo simulations of a simple inverse ECGI source localisation model with error sampling to understand the variations in ECGI solutions. Second, we present multiple visualisation techniques, including confidence maps, level-sets, and topology-based visualisations, to better understand uncertainty in source localization. Our approach offers a new way to study uncertainty in the ECGI pipeline. [ABSTRACT FROM AUTHOR]

In recent years, the impact of measurement errors on the performance of various control charts have been well evaluated. However, as far as we know, the multiple sampling control charts in the literature have been proposed based on the assumption that the measurements are precise. In this paper, we investigate the effect of measurement errors with both constant and linearly increasing variance on the performance of double sampling (DS)- X ¯ chart. We study the capability of multiple measurements procedure to reduce the undesired effect of imprecise measurements on detection capability of DS- X ¯ chart. Using simulation experiments, we compare the performance of DS- X ¯ and classical X ¯ charts in terms of average run length (ARL) and standard deviation of run length (SDRL) metrics. The results show that the measurement errors negatively affect the performance of both DS- X ¯ and classical X ¯ charts. The comparison results also confirm that under measurement errors, the DS- X ¯ control chart outperforms the classical X ¯ chart in all out-of-control scenarios. Moreover, the results indicate that taking several measurements on each item can effectively reduce the undesired impact of error on chart's performance. Finally, the undesired impact of measurement errors on detection capability of DS- X ¯ control chart is illustrated using a real-life example. [ABSTRACT FROM AUTHOR]

Event-triggered control, as networked systems are popular, is increasingly appreciated. Such control could arouse essential saving of communication/computation and would lay the groundwork for networked (control) systems. However, due to the discontinuity and hybrid nature of event-triggered control, the exclusion of Zeno phenomenon is the premise of its workability. Aiming to circumvent the Zeno behaviour more explicitly, we attach a dynamic dwell time to triggering mechanism and propose a new adaptive event-triggered control scheme for uncertain nonlinear systems. Specifically, a set of dynamic gains are first introduced to compensate for large uncertainties and nonlinearities, and the indispensability of a set of gains rather than a gain is elaborated on. Then, from a dynamic-gain-dependent monitoring rule, the positive dwell time is generated so that the Zeno phenomenon can be excluded automatically. In particular, the rule enables system behaviour to be always monitored by evaluating the sampling errors of gains. Based on this, we construct an adaptive controller with linear structure, which allows the execution error can be estimated explicitly, and in turn suppressed by the gains. It is shown that, with the proposed event-triggered control, all the closed-loop system signals are bounded and the system state ultimately converges to zero. Notably, the designed triggering mechanism could save more resources in theory since the event detection and gains monitoring are performed alternately. The effectiveness and superiority of the proposed scheme is illustrated by two simulation examples. [ABSTRACT FROM AUTHOR]

Apart from the sampling errors related to the sample preparation protocol in a laboratory to determine the ash and sulfur content of the coal, the delimitation/extraction errors (recovery, among other geological factors) and the "extension" error must be considered. As the collection time interval increases, the extension error also increases, so that even automatic sampling has an error that is often completely ignored. The extension error was determined via a variographic test, which was specifically designed for this study. This test was proposed to be carried out at the mining company to check the uncertainties (bias and reproducibility errors), regarding the sampling at the client. In the latter, sampling is done in piles with manual samplers. At the mining company, there are automatic collectors, but the sample interval is not considered when calculating the total error. To redeem reconciliation problems between the mining company and the client, as well as to avoid contractual fines, it was decided to compare the results of the variographic analysis carried out in the mining company with those obtained by the client. These showed considerable bias and extension errors according to the collection time intervals in the mining company, especially regarding sulfur. In shorter time intervals, 30 minutes, extension errors have resulted in a range of [0.18% to 1.52%] and [29% to 59%] for sulfur and ashes respectively (CI = 95%). For longer intervals, there would be a doubt about accepting or not the lot: maximum limits are 1.5% for sulfur and 55% for ashes. Also, as extension errors are elevated and possibly higher for "true mass flows," which exceed 2,000 t/h, it was recommended to adapt the automatic samplers of the mining company at shorter intervals than it was used to do (2/2 hours). Then, it will improve the processes control, including blending, avoiding reconciliation problems, and subsequent contractual fines. The client was alerted to the problem of bias which occurs when the coal is sampled manually in piles. For both, it is important to know the intrinsic variance of the material/lot and the errors related to the sampling. [ABSTRACT FROM AUTHOR]

The potential of satellite imagery to complement the in-situ gauge networks in monitoring freshwater is well established. However, Temporal Sampling (TS) intervals of satellite altimetry impart significant uncertainty compared to daily or sub-daily in-situ gauge measurements. This study examines the effect of infrequent TS from satellite altimetry, i.e. 10-Day (Jason-2/3), 21-Day (SWOT), 27-Day (Sentinel-3A) and 35-Day (SARAL/AltiKa) for the evaluation of reservoir storage and outflow. The metrics of Relative Error (RE), Root Mean Squared Deviation (RMSD), and the correlation between altimetry-based and in-situ observations have been used for the evaluation. Results show that the altimeters having a high TS frequency, such as 10-Day or 21-Day, performed well with RMSD less than 0.2m and correlation more than 98% in estimating satellite-based storage and outflow compare to coarse 27-Day or 35-Day TS. This study improves the understanding of sampling error in satellite altimetry, which has immense potential for future SWOT altimeter mission. [ABSTRACT FROM AUTHOR]

Researchers in statistical shape analysis often analyze outlines of objects. Even though these contours are infinite-dimensional in theory, they must be discretized in practice. When discretizing, it is important to reduce the number of sampling points considerably to reduce computational costs, but to not use too few points so as to result in too much approximation error. Unfortunately, determining the minimum number of points needed to achieve sufficiently approximate the contours is computationally expensive. In this paper, we fit regression models to predict these lower bounds using characteristics of the contours that are computationally cheap as predictor variables. However, least squares regression is inadequate for this task because it treats overestimation and underestimation equally, but underestimation of lower bounds is far more serious. Instead, to fit the models, we use the LINEX loss function, which allows us to penalize underestimation at an exponential rate while penalizing overestimation only linearly. We present a novel approach to select the shape parameter of the loss function and tools for analyzing how well the model fits the data. Through validation methods, we show that the LINEX models work well for reducing the underestimation for the lower bounds. [ABSTRACT FROM AUTHOR]

In this paper, we design an innovative and general class of modified two-stage sampling schemes to enhance double sampling and modified double sampling procedures. Under the squared error loss plus linear cost of sampling, we revisit the classic problem of minimum risk point estimation (MRPE) for an unknown normal mean μ ( ∈ R ) when the population variance σ 2 ( ∈ R + ) also remains unknown. With stopping variables constructed based on an arbitrary general estimator Wm for σ, which satisfies a set of certain conditions, our procedures are proved to enjoy asymptotic first- and second-order efficiency as well as asymptotic first-order risk efficiency. For illustrative purposes, we further investigate specific modified two-stage MRPE procedures, where we substitute appropriate multiples of sample standard deviation, Gini's mean difference (GMD), and mean absolute deviation (MAD) in the place of Wm, respectively. Extensive simulation studies are utilized to validate our theoretical findings. A real-life data set of weight change from female anorexic patients is then analyzed to demonstrate the practical applicability of these modified two-stage MRPE procedures. Comparing them in the case where there exist suspect outliers in the pilot sample, we are empirically confident that the GMD- and MAD-based procedures appear more robust than the sample-standard-deviation-based procedures. [ABSTRACT FROM AUTHOR]

The ratio, product, difference estimators, and unbiased estimator under systematic sampling scheme has been studied in the presence of measurement error. The methods of estimation have been proposed for the estimation of the finite population mean. To exhibit the effect of measurement error, the study variable and auxiliary variable are supposed to be observed with measurement error. The properties of estimators' viz. bias and MSE have been obtained. The simulation study has been conducted to compute the effect of measurement error on the MSE for the different levels of correlation coefficient and different levels of error variance. [ABSTRACT FROM AUTHOR]

Noninferiority (NI) trial designs are often used to compare a new treatment with an active control for treating diseases for which use of a placebo control in a trial is ethically unacceptable. The validity of conclusion from an NI trial relies on some critical assumptions such as constancy assumption. This assumption, often unverifiable in a two-arm NI trial without a placebo control, refers to as the similarity of the active control effect in the current NI trial to that in the historical trials. Violation of the constancy assumption may lead to not only the inflation of Type I error, but also invalid conclusion of the NI trial. In this article, we propose a probabilistic bias analysis of historical data using the Bayesian hierarchical modeling framework to assess heterogeneity of active control effect and hence the constancy assumption. Both random sampling error and heterogeneity in the active control effect across historical trials will be accounted in the analysis of the current NI trial. The proposed method is illustrated using a real example of NI trial with a binary endpoint. [ABSTRACT FROM AUTHOR]

The flow velocity of exhaust gas from an industrial stack varies from facility to facility and can easily change over time, even in a stack. To accurately measure the concentration of fine particles such as PM10 or PM2.5 emitted in such environments, the conditions for isokinetic sampling must be satisfied. In this study, a novel sampling probe that generates a small error in particle concentration in variable flow velocity environments was developed, even though isokinetic sampling was not performed. The mass distributions obtained by isokinetic sampling using a conventional sampling probe at a flow velocity of 0–6 m/s were compared with those measured using the developed probe to verify the performance of the proposed probe. It was confirmed that the two mass distributions are similar within an error of 5% under all flow velocity conditions. The mass concentrations estimated through theoretical correction considering sampling loss or gain in non-isokinetic environments were also compared, and the distributions were quite similar within approximately 5%. This verifies that the proposed sampling probe can perform nearly perfect sampling of fine particles without changing the sampling flow rates, with minimal particle under- or overestimation caused by non-isokinetic sampling for variable flow velocity conditions. Copyright © 2022 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

The present investigation deals with the estimation of population variance in successive sampling in presence and absence of measurement error. We have suggested a chain type estimator of population variance and compared its performances with natural sample variance estimator in presence and absence of measurement errors. The dominance of the proposed strategies over conventional ones has been demonstrated through numerical investigation carried over the dataset of natural and artificially generated population. Suitable recommendations have been made for the application of proposed strategy in real life. [ABSTRACT FROM AUTHOR]

Comparative biologists are often interested in inferring covariation between multiple biological traits sampled across numerous related taxa. To properly study these relationships, we must control for the shared evolutionary history of the taxa to avoid spurious inference. An additional challenge arises as obtaining a full suite of measurements becomes increasingly difficult with increasing taxa. This generally necessitates data imputation or integration, and existing control techniques typically scale poorly as the number of taxa increases. We propose an inference technique that integrates out missing measurements analytically and scales linearly with the number of taxa by using a post-order traversal algorithm under a multivariate Brownian diffusion (MBD) model to characterize trait evolution. We further exploit this technique to extend the MBD model to account for sampling error or nonheritable residual variance. We test these methods to examine mammalian life history traits, prokaryotic genomic and phenotypic traits, and HIV infection traits. We find computational efficiency increases that top two orders-of-magnitude over current best practices. While we focus on the utility of this algorithm in phylogenetic comparative methods, our approach generalizes to solve long-standing challenges in computing the likelihood for matrix-normal and multivariate normal distributions with missing data at scale. for this article are available online. [ABSTRACT FROM AUTHOR]

This paper considers the problem of estimation of population mean of the study character in two-occasion successive sampling in presence of non-response and measurement error. A logarithmic type imputation technique has been developed to reduce the nuisance effect of non-response in sample surveys. Utilizing information available on a highly positively or highly negatively correlated auxiliary variable, a logarithmic type estimator is proposed and its properties, including bias and mean square error, are discussed. An optimal replacement policy has been derived, and effect of measurement errors on the mean square error of the estimator has been studied. Empirical studies have been carried out using both real and simulated data, and suitable recommendations have been put forward to the survey statisticians for applications in real life problems. [ABSTRACT FROM AUTHOR]

In the presence of non-normally distributed asset returns, an optimal portfolio selection should consider higher-order (co-)moments when no sampling errors exist. However, the curse of dimensionality has already been a serious concern in mean-variance analyses; higher-order (co-)moments also face the same dilemma. This study uses mixed-frequency (MF) data under the assumption that stock returns are generated by a mixed-data sampling (MIDAS) regression model, which shows to provide well-improved estimates for the covariance, co-skewness, and co-kurtosis matrices for higher dimension. We discover that the new, improved estimates used in higher-order (co-)moment portfolio selections can dominate the other existing structured estimates from an out-of-sample perspective in most instances. [ABSTRACT FROM AUTHOR]

The present study was conducted to determine the extent of observation of patients' privacy by physicians and nurses and its relationship with patient satisfaction. This descriptive correlational study was conducted on 600 patients selected by convenience sampling. Based on the patients' points of view, the level of observation of patients' privacy and its dimensions, especially the psychosocial dimension, were reported to be higher in nurses than in physicians. Pearson's correlation coefficient showed a direct and significant relationship between the observation of privacy by the nurses and physicians and different dimensions of patient satisfaction. [ABSTRACT FROM AUTHOR]

Extended phase-space isokinetic methods in their deterministic [Minary et al., Phys. Rev. Lett. 93, 150201 (2004)] and stochastic forms [Leimkuhler et al., Mol. Phys. 111, 3579 (2013)] have proved tremendously successful in allowing multiple time-scale molecular dynamics simulations to be performed with very large time steps. These methods work by coupling the physical degrees of freedom to a set of Nosé-Hoover chain or Nosé-Hoover Langevin thermostats via an isokinetic constraint, which has the effect of avoiding resonance artifacts that plague multiple time-step algorithms. In this paper, we introduce a new resonance-free approach that achieves the same gains in time step but without the imposition of isokinetic constraints or the introduction of extended phase-space variables. Rather, we modify the physical Hamiltonian that effects the same regulation of resonances achieved by the isokinetic constraints. In so doing, we show that sampling errors can be controlled and performance improvements are possible within a simpler Hamiltonian framework. The method is demonstrated in simulations of the structure of liquid water and, in conjunction with enhanced sampling, in generation of the Ramachandran free-energy surface of the solvated alanine dipeptide. [ABSTRACT FROM AUTHOR]

Erroneous enumerations in the census include multiple enumerations and other errors. The linear estimator for estimating these errors currently used in several countries leads to underestimation when the sample used for the estimation comprises few of these errors. The "ratio estimator" of the total number of erroneous enumerations overcomes this difficulty. This is the one used by China for the 2020 census. Empirical analysis shows that the ratio estimator provides a smaller sampling error than the linear estimator. [ABSTRACT FROM AUTHOR]

In this article, we propose a new method in estimating non parametric regression curve. This method combines the smoothing Spline and Kernel functions. Estimation of the estimator is completed by minimizing penalized least square. To see the performance of the model, this model is applied to simulation data with a variety of sample sizes and error variances. Then, the model is applied to the Unemployment Rate data in East Java Province, Indonesia. The results show that this model provides good performance in modeling data and predictions. [ABSTRACT FROM AUTHOR]

The measurement error (ME), one of the serious kinds of non sampling errors, is the difference between the value of a characteristic provided by the respondent and the true value of it. Generally, in practice it is almost impossible to get the true value due to non availability of an appropriate and feasible method of measuring it. In fact, the assumption of getting information from a unit without any biases or measuring a unit correctly is very unrealistic. ME may give rise to serious bias and variable errors both in an estimator obtained through a survey. This work is mainly concentrated on the study of variations that may occur in certain components of ME models considered and their impact on some existing usual estimators of population mean along with a proposed family of exponential-type estimators which might be considered as an alternative to classical ratio/product estimators in some sense. Some important features of these estimators in presence of ME have been dealt with. Results discussed have been highlighted with the help of an empirical data which reflects the extent of consequences of changes in model parameters on the performance of estimators. [ABSTRACT FROM AUTHOR]

Modelling movement uncertainty is of profound significance in promoting effective trajectory analysis and mining. However, classic uncertainty models are limited by rigid assumptions on moving speed and distance, which ignores the stochastic nature of individual's travel behaviour. This study introduces a novel method using adaptive ellipses to represent the movement uncertainty in a planar space under the framework of time geography. Two models are established by considering different error sources in trajectory data. The first model captures the uncertainty caused by sampling error, and the second one further, takes the measurement error into account. The Minkowski distance metric is adopted to determine the size of uncertainty ellipses, in which the Minkowski parameter is optimized for each segment in the raw trajectory on the basis of the geometric characteristics extracted. Compared with state-of-the-art methods on five real-life trajectory datasets, the proposed method is proved to produce more effective uncertain regions, which significantly reduce the redundant uncertain area, while retaining at a comparative level of actual movement coverage. Given the heterogeneity of human mobility patterns, this study provides a robust and applicable solution for adaptively modelling individual's movement uncertainty, which is expected to benefit trajectory-related applications in various scenarios. [ABSTRACT FROM AUTHOR]