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The authors discuss the problem of replication in computer science research. They mention the typical format for periodical articles reporting on a hypothesis and its testing, the effects of publication biases particularly on articles that present a null hypothesis, and present suggestions for improving the process.

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]

An important question in many machine learning applications is whether two samples arise from the same generating distribution. Although an old topic in Statistics, simple accept/reject decisions given by most hypothesis tests are often not enough: it is well known that the rejection of the null hypothesis does not imply that differences between the two groups are meaningful from a practical perspective. In this work, we present a novel nonparametric approach to visually assess the dissimilarity between the datasets that goes beyond two-sample testing. The key idea of our approach is to measure the distance between two (possibly) high-dimensional datasets using variational autoencoders. We also show how this framework can be used to create a formal statistical test to test the hypothesis that both samples arise from the same distribution. We evaluate both the distance measurement and hypothesis testing approaches on simulated and real world datasets. The results show that our approach is useful for data exploration (as it, for instance, allows for quantification of the discrepancy/separability between categories of images), which can be particularly helpful in early phases of the a machine learning pipeline. [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]

Ensemble classifiers using clustering have significantly improved classification and prediction accuracies of many systems. These types of ensemble approaches create multiple clusters to train the base classifiers. However, the problem with this is that each class might have many clusters and each cluster might have different number of samples, so an ensemble decision based on large number of clusters and different number of samples per class within a cluster produces biased and inaccurate results. Therefore, in this article, we propose a novel methodology to create an appropriate number of strong data clusters for each class and then balance them. Furthermore, an ensemble framework is proposed with base classifiers trained on strong and balanced data clusters. The proposed approach is implemented and evaluated on 24 benchmark data sets from the University of California Irvine (UCI) machine learning repository. An analysis of results using the proposed approach and the existing state-of-the-art ensemble classifier approaches is conducted and presented. A significance test is conducted to further validate the efficacy of the results and a detailed analysis is presented. [ABSTRACT FROM AUTHOR]

• A multi-view spectral clustering algorithm is proposed to take decision in uncertain systems. • The proposed algorithm relies on co-regularization. • A symmetry-favored graph is constructed to design affinity matrix for each view. • A self-adaptive mixture similarity measure is used to construct a graph efficiently. In the machine learning and pattern recognition fraternity, uncertain data clustering is an essential job because uncertainty in data makes the clustering process more difficult. Recently, multi-view clustering is gaining more attention towards data miners for certain data because it produces good results compared to grouping based on a single viewpoint. In uncertain data clustering, similarity measure plays an imperative role. However, state-of-the-art similarity measures suffer from several limitations. For example, when two distributions of two uncertain data are heavily overlapped in locations, then Geometric similarity measure alone is not sufficient. On the other hand, similarity measure based on probability distribution is not enough when two uncertain data are not closed to each other or completely separated. In this study, induced kernel distance and Jeffrey-divergence are fused by the degree of overlap concerning each view of a dataset to construct a self-adaptive mixture similarity measure (SAM). The SAM is further used with pairwise co-regularization in multi-view spectral clustering for grouping uncertain data. The proof of convergence of the objective function of the proposed clustering algorithm is also presented in this study. All the experiments are carried out on nine real-world deterministic datasets, three real-life and one synthetic uncertain datasets. Nine real-world deterministic datasets are further converted into uncertain datasets before executing all the clustering algorithms. Experimental results illustrate that the proposed algorithm outperforms nine state-of-the-art methods. The comparison is made using five clustering evaluation metrics. The proposed method is also tested using null hypothesis significance tests. [ABSTRACT FROM AUTHOR]

• Propose a novel large group decision-making method from statistical perspective. • Utilize statistics concept to measure and reduce decision risk. • Take into account risk attitudes of DMs throughout the decision-making process. • Use a case, comparison and sensitivity analyses to testify the validity of the method. In this study, we propose a statistical method incorporating decision risk and risk attitude into large group decision-making. The decision-making groups are divided into subgroups based on their attitudes to risk, and the evaluation information for the decision-makers within the same subgroup is combined to form the sample dataset. Next, the internal decision risk levels of all subgroups are measured using sample standard deviations and reduced through a feedback mechanism. Significance testing is used to determine the criterion weights and to measure the external decision risk levels of subgroups. The internal and external decision risk levels are then combined to yield the subgroup weights. Confidence interval is used to transform the sample data into interval numbers, which are then aggregated and analyzed to yield the decision-making results. Meanwhile, risk attitudes are taken into account throughout the decision-making process by various means. A case study and comparison analyses, along with sensitivity analyses, are used to illustrate the feasibility and rationality of the proposed method. Our experiments suggest that decision risk and risk attitude matter in large group decision-making. [ABSTRACT FROM AUTHOR]

One of the most challenging problems in the field of online learning is concept drift, which deeply influences the classification stability of streaming data. If the data stream is imbalanced, it is even more difficult to detect concept drifts and make an online learner adapt to them. Ensemble algorithms have been found effective for the classification of streaming data with concept drift, whereby an individual classifier is built for each incoming data chunk and its associated weight is adjusted to manage the drift. However, it is difficult to adjust the weights to achieve a balance between the stability and adaptability of the ensemble classifiers. In addition, when the data stream is imbalanced, the use of a size-fixed chunk to build a single classifier can create further problems; the data chunk may contain too few or even no minority class samples (i.e., only majority class samples). A classifier built on such a chunk is unstable in the ensemble. In this article, we propose a chunk-based incremental learning method called adaptive chunk-based dynamic weighted majority (ACDWM) to deal with imbalanced streaming data containing concept drift. ACDWM utilizes an ensemble framework by dynamically weighting the individual classifiers according to their classification performance on the current data chunk. The chunk size is adaptively selected by statistical hypothesis tests to access whether the classifier built on the current data chunk is sufficiently stable. ACDWM has four advantages compared with the existing methods as follows: 1) it can maintain stability when processing nondrifted streams and rapidly adapt to the new concept; 2) it is entirely incremental, i.e., no previous data need to be stored; 3) it stores a limited number of classifiers to ensure high efficiency; and 4) it adaptively selects the chunk size in the concept drift environment. Experiments on both synthetic and real data sets containing concept drift show that ACDWM outperforms both state-of-the-art chunk-based and online methods. [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]

Reliability enhancement testing (RET) is an accelerated testing which hastens the performance degradation process to surface its inherent defects of design and manufacture. It is an important hypothesis that the degradation mechanism of the RET is the same as the one of the normal stress condition. In order to check the consistency of two mechanisms, we conduct two enhancement tests with a missile servo system as an object of the study, and preprocess two sets of test data to establish the accelerated degradation models regarding the temperature change rate that is assumed to be the main applied stress of the servo system during the natural storage. Based on the accelerated degradation models and natural storage profile of the servo system, we provide and demonstrate a procedure to check the consistency of two mechanisms by checking the correlation and difference of two sets of degradation data. The results indicate that the two degradation mechanisms are significantly consistent with each other. [ABSTRACT FROM AUTHOR]

Abstract Estimation and hypothesis tests for partial linear multiplicative models are considered in this paper. A profile least product relative error estimation method is proposed to estimate unknown parameters. We employ the smoothly clipped absolute deviation penalty to do variable selection. A Wald-type test statistic is proposed to test a hypothesis on parametric components. The asymptotic properties of the estimators and test statistics are established. We also suggest a score-type test statistic for checking the validity of partial linear multiplicative models. The quadratic form of the scaled test statistic has an asymptotic chi-squared distribution under the null hypothesis and follows a non-central chi-squared distribution under local alternatives, converging to the null hypothesis at a parametric convergence rate. We conduct simulation studies to demonstrate the performance of the proposed procedure and a real data is analyzed to illustrate its practical usage. [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]

Concept drift detectors are software that usually attempt to estimate the positions of concept drifts in large data streams in order to replace the base learner after changes in the data distribution and thus improve accuracy. Statistical Test of Equal Proportions (STEPD) is a simple, efficient, and well-known method which detects concept drifts based on a hypothesis test between two proportions. However, statistically, this test is not recommended when sample sizes are small or data are sparse and/or imbalanced. This article proposes an ingeniously efficient implementation of the statistically preferred but computationally expensive Fisher’s Exact test and examines three slightly different applications of this test for concept drift detection, proposing FPDD, FSDD, and FTDD. Experiments run using four artificial dataset generators, with both abrupt and gradual drift versions, as well as three real-world datasets, suggest that the new methods improve the accuracy results and the detections of STEPD and other well-known and/or recent concept drift detectors in many scenarios, with little impact on memory and run-time usage. [ABSTRACT FROM AUTHOR]

We consider the problem of distributed learning, where a network of agents collectively aim to agree on a hypothesis that best explains a set of distributed observations of conditionally independent random processes. We propose a distributed algorithm and establish consistency, as well as a nonasymptotic, explicit, and geometric convergence rate for the concentration of the beliefs around the set of optimal hypotheses. Additionally, if the agents interact over static networks, we provide an improved learning protocol with better scalability with respect to the number of nodes in the network. [ABSTRACT FROM PUBLISHER]

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]

We consider estimation and hypothesis test for partial linear measurement errors models when the response variable and covariates in the linear part are measured with additive distortion measurement errors, which are unknown functions of a commonly observable confounding variable. We propose a transformation based profile least squares estimator to estimate unknown parameter under unrestricted and restricted conditions. Asymptotic properties for the estimators are established. To test a hypothesis on the parametric components, a test statistic based on the normalized difference between the residual sums of squares under the null and alternative hypotheses is proposed, and we further show that its limiting distribution is a standard chi-squared distribution. Lastly, we suggest a lack-of-fit test of score type for checking the validity of partial linear models. The quadratic form of the scaled test statistic is asymptotically chi-squared under the null hypothesis and a non-centered one under local alternatives converging to the null hypothesis at parametric rates. Simulation studies are conducted to demonstrate the performance of the proposed procedure and a real example is analyzed for an illustration. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic analysis of Euler-Bernoulli beams is performed with the time-dependent Boundary Element Method formulation (TD-BEM). In the standard formulation, the variables related to the essential boundary conditions (displacement and rotation) are assumed to vary linearly in time, i.e., within each time interval, whereas the variables related to the natural boundary conditions (shear force and bending moment) are assumed to have a constant time variation. Different hypothesis concerning the time behavior of these quantities lead to unstable and inaccurate results. In the linear θ method, on the other hand, all the variables are assumed to have a linear time variation and reliable results are achieved. These results can be seen in the examples presented in this article, which contain the four usual types of beams under continuously distributed and concentrated loadings. [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]

Based on the random matrix theory, a unified numerical approach is developed for power calculation in the general framework of hypothesis testing with high-dimensional covariance matrices. In the central limit theorem of linear spectral statistics for sample covariance matrices, the theoretical mean and covariance are computed numerically. Based on these numerical values, the power of the hypothesis test can be evaluated, and furthermore the confidence interval for the unknown parameters in the high-dimensional covariance matrix can be constructed. The validity of the proposed algorithms is well supported by a convergence theorem. Our numerical method is assessed by extensive simulation studies, and a real data example of the S & P 100 index data is analyzed to illustrate the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a prior near-ignorance model for regression based on a set of Gaussian Processes (GP). GPs are natural prior distributions for Bayesian regression. They offer a great modeling flexibility and have found widespread application in many regression problems. However, a GP requires the prior elicitation of its mean function, which represents our prior belief about the shape of the regression function, and of the covariance between any two function values. In the absence of prior information, it may be difficult to fully specify these infinite dimensional parameters. In this work, by modeling the prior mean of the GP as a linear combination of a set of basis functions and assuming as prior for the combination coefficients a set of conjugate distributions obtained as limits of truncate exponential priors, we have been able to model prior ignorance about the mean of the GP. The resulting model satisfies translation invariance, learning and, under some constraints, convergence, which are desirable properties for a prior near-ignorance model. Moreover, it is shown in this paper how this model can be extended to allow for a weaker specification of the GP covariance between function values, by letting each basis function to vary in a set of functions. Application to hypothesis testing has shown how the use of this model induces the capability of automatically detecting when a reliable decision cannot be made based on the available data. [ABSTRACT FROM AUTHOR]

Tests for the joint null hypothesis of a unit root based on the components representation of a time series are developed. The proposed testing procedure is designed to detect a unit root as well as guide the practitioner regarding the specification of trend component of a time series. The limiting null distributions of the newly developed F-statistics are derived. Finite sample simulation evidence shows that the F-statistics maintain their size, and have power against the trend-break stationary alternative. The use of our methodology is illustrated through an empirical examination of the US–UK real exchange rate, the UK industrial production, and the UK CPI series. [ABSTRACT FROM AUTHOR]

The problem of testing the population mean vector of high-dimensional multivariate data is considered. Inspired by Roy’s union–intersection test, a generalized high-dimensional likelihood ratio test for the normal population mean vector is proposed. The p -value for the test is obtained by using randomization method, which does not rely on assumptions about the structure of the covariance matrix. An interpretation of the new statistic is given, which does not rely on the normality assumption. Hence the proposed test is also available for non-normal multivariate population. Simulation studies show that the new test offers higher power than other two competing tests when the variables are dependent and performs particularly well for non-normal multivariate population. [ABSTRACT FROM AUTHOR]

A test for ordered categorical variables is of considerable importance, because they are frequently encountered in biomedical studies. This paper introduces a simple ordering test approach for the two-way r × c contingency tables with incomplete counts by developing six test statistics, i.e., the likelihood ratio test statistic, score test statistic, global score test statistic, Hausman–Wald test statistic, Wald test statistic and distance-based test statistic. Bootstrap resampling methods are also presented. The performance of the proposed tests is evaluated with respect to their empirical type I error rates and empirical powers. The results show that the likelihood ratio test statistic based on the bootstrap resampling methods perform satisfactorily for small to large sample sizes. A real example from a wheeze study in six cities is used to illustrate the proposed methodologies. [ABSTRACT FROM AUTHOR]

Boltzmann machines (BMs) are a class of binary neural networks for which there have been numerous proposed methods of estimation. Recently, it has been shown that in the fully visible case of the BM, the method of maximum pseudolikelihood estimation (MPLE) results in parameter estimates, which are consistent in the probabilistic sense. In this brief, we investigate the properties of MPLE for the fully visible BMs further, and prove that MPLE also yields an asymptotically normal parameter estimator. These results can be used to construct confidence intervals and to test statistical hypotheses. These constructions provide a closed-form alternative to the current methods that require Monte Carlo simulation or resampling. We support our theoretical results by showing that the estimator behaves as expected in simulation studies. [ABSTRACT FROM AUTHOR]

Large covariance matrices play a fundamental role in various high-dimensional statistics. Investigating the limiting behavior of the eigenvalues can reveal informative structures of large covariance matrices, which is particularly important in high-dimensional principal component analysis and covariance matrix estimation. In this paper, we propose a framework to test the number of distinct population eigenvalues for large covariance matrices, i.e. the order of a Population Spectral Distribution. The limiting distribution of our test statistic for a Population Spectral Distribution of order 2 is developed along with its ( N , p ) consistency, which is clearly demonstrated in our simulation study. We also apply our test to two classical microarray datasets. [ABSTRACT FROM AUTHOR]

PSYCHOLOGY IS in a bit of trouble these days. It has made headlines for questionable interpretations of statistics and for wellknown studies that can't be replicated. Other quantitative social and [...]

The problem of testing the mean vector in a high-dimensional setting is considered. Up to date, most high-dimensional tests for the mean vector only make use of the marginal information from the variables, and do not incorporate the correlation information into the test statistics. A new testing procedure is proposed, which makes use of the covariance information between the variables. The new approach is novel in that it can select important variables that contain evidence against the null hypothesis and reduce the impact of noise accumulation. Simulations and real data analysis demonstrate that the new test has higher power than some competing methods proposed in the literature. [ABSTRACT FROM AUTHOR]

A class of goodness-of-fit tests whose test statistic is an L 2 norm of the difference of the empirical characteristic function of the sample and a parametric estimate of the characteristic function in the null hypothesis, is considered. The null distribution is usually estimated through a parametric bootstrap. Although very easy to implement, the parametric bootstrap can become very computationally expensive as the sample size, the number of parameters or the dimension of the data increase. It is proposed to approximate the null distribution through a weighted bootstrap. The method is studied both theoretically and numerically. It provides a consistent estimator of the null distribution. In the numerical examples carried out, the estimated type I errors are close to the nominal values. The asymptotic properties are similar to those of the parametric bootstrap but, from a computational point of view, it is more efficient. [ABSTRACT FROM AUTHOR]

This paper proposes a chi-square method (CSM) to obtain a priority vector for group decision making (GDM) problems where decision-makers’ (DMs’) assessment on alternatives is furnished as incomplete reciprocal preference relations with missing values. Relevant theorems and an iterative algorithm about CSM are proposed. Saaty’s consistency ratio concept is adapted to judge whether an incomplete reciprocal preference relation provided by a DM is of acceptable consistency. If its consistency is unacceptable, an algorithm is proposed to repair it until its consistency ratio reaches a satisfactory threshold. The repairing algorithm aims to rectify an inconsistent incomplete reciprocal preference relation to one with acceptable consistency in addition to preserving the initial preference information as much as possible. Finally, four examples are examined to illustrate the applicability and validity of the proposed method, and comparative analyses are provided to show its advantages over existing approaches. [ABSTRACT FROM AUTHOR]

In this paper, the noise enhanced system performance in a binary hypothesis testing problem is investigated when the additive noise is a convex combination of the optimal noise probability density functions (PDFs) obtained in two limit cases, which are the minimization of false-alarm probability ( P FA ) without decreasing detection probability ( P D ) and the maximization of P D without increasing P FA , respectively. Existing algorithms do not fully consider the relationship between the two limit cases and the optimal noise is often deduced according to only one limit case or Bayes criterion. We propose a new optimal noise framework which utilizes the two limit cases and deduce the PDFs of the new optimal noise. Furthermore, the sufficient conditions are derived to determine whether the performance of the detector can be improved or not via the new noise. In addition, the effects of the new noise are analyzed according to Bayes criterion. Rather than adjusting the additive noise again as shown in other algorithms, we just tune one parameter of the new optimal noise PDF to meet the different requirements under the Bayes criterion. Finally, an illustrative example is presented to study the theoretical results. [ABSTRACT FROM AUTHOR]

Frequent episode discovery is one of the methods used for temporal pattern discovery in sequential data. An episode is a partially ordered set of nodes with each node associated with an event type. For more than a decade, algorithms existed for episode discovery only when the associated partial order is total (serial episode) or trivial (parallel episode). Recently, the literature has seen algorithms for discovering episodes with general partial orders. In frequent pattern mining, the threshold beyond which a pattern is inferred to be interesting is typically user-defined and arbitrary. One way of addressing this issue in the pattern mining literature has been based on the framework of statistical hypothesis testing. This paper presents a method of assessing statistical significance of episode patterns with general partial orders. A method is proposed to calculate thresholds, on the non-overlapped frequency, beyond which an episode pattern would be inferred to be statistically significant. The method is first explained for the case of injective episodes with general partial orders. An injective episode is one where event-types are not allowed to repeat. Later it is pointed out how the method can be extended to the class of all episodes. The significance threshold calculations for general partial order episodes proposed here also generalize the existing significance results for serial episodes. Through simulations studies, the usefulness of these statistical thresholds in pruning uninteresting patterns is illustrated. [ABSTRACT FROM AUTHOR]

The article discusses the growing realization that statistically significant claims in scientific publications are routinely mistaken. The authors explain researchers' use of the p-value (probability) to express the confidence of their data against a null hypothesis and discuss how to test a hypothesis and research by Michael Peterson and colleagues who claimed to have found a statistical association, expressed as a p-value, between arm strength and socioeconomic status.

The class of composite likelihood functions provides a flexible and powerful toolkit to carry out approximate inference for complex statistical models when the full likelihood is either impossible to specify or unfeasible to compute. However, the strength of the composite likelihood approach is dimmed when considering hypothesis testing about a multidimensional parameter because the finite sample behavior of likelihood ratio, Wald, and score-type test statistics is tied to the Godambe information matrix. Consequently, inaccurate estimates of the Godambe information translate in inaccurate p-values. The approach based on a fully nonparametric saddlepoint test statistic derived from the composite score functions is shown to achieve accurate inference. The proposed statistic is asymptotically chi-squared distributed up to a relative error of second order and does not depend on the Godambe information. The validity of the method is demonstrated through simulation studies. [ABSTRACT FROM AUTHOR]

Multiple-testing problems have received much attention. Different strategies have been considered in order to deal with this problem. The false discovery rate (FDR) is, probably, the most studied criterion. On the other hand, the sequential goodness of fit (SGoF), is a recently proposed approach. Most of the developed procedures are based on the independence among the involved tests; however, in spite of being a reasonable proviso in some frameworks, independence is not realistic for a number of practical cases. Therefore, one of the main problems in order to develop appropriate methods is, precisely, the effect of the dependence among the different tests on decisions making. The consequences of the correlation on the z-values distribution in the general multitesting problem are explored. Some different algorithms are provided in order to approximate the distribution of the expected rejection proportions. The performance of the proposed methods is evaluated in a simulation study in which, for comparison purposes, the Benjamini and Hochberg method to control the FDR, the Lehmann and Romano procedure to control the tail probability of the proportion of false positives (TPPFP), and the Beta--Binomial SGoF procedure are considered. Three different dependence structures are considered. As usual, for a better understanding of the problem, several practical cases are also studied. [ABSTRACT FROM AUTHOR]