Article
Statistics & Probability
Nayel Bettache, Cristina Butucea, Marianne Sorba
Summary: This study examines n independent p-dimensional Gaussian vectors with covariance matrix having a Toeplitz structure, aiming to test the independence of the vectors and select the support of non-zero entries. Test procedures are developed, showing non-asymptotic behavior under one-sided and two-sided alternatives. Results also extend to nearly Toeplitz covariance structure and sub-Gaussian vectors, with numerical results indicating excellent performance of the test procedures and support selectors, particularly as dimension p increases.
JOURNAL OF MULTIVARIATE ANALYSIS
(2022)
Article
Biology
Erika S. Helgeson, David M. Vock, Eric Bair
Summary: This paper proposes a novel method to evaluate the significance of identified clusters by comparing the explained variation due to clustering from the original data to a unimodal reference distribution that preserves the covariance structure in the data. The approach is adapted for high-dimension low-sample size settings and can be used to test the null hypothesis and determine the optimal number of clusters.
Editorial Material
Obstetrics & Gynecology
Philip M. Sedgwick, Anne Hammer, Ulrik Schioler Kesmodel, Lars Henning Pedersen
Summary: Traditional null hypothesis significance testing (NHST) is widely used in obstetric and gynecological research, but its application in inferring clinical significance is controversial. Misinterpretation of statistical significance and ignorance of NHST limitations may lead to false claims and dismissal of important factors.
ACTA OBSTETRICIA ET GYNECOLOGICA SCANDINAVICA
(2022)
Article
Psychology, Multidisciplinary
Herbert Hoijtink
Summary: Researchers increasingly use Bayes factor for hypotheses evaluation, with NHBT being sensitive to the specification of the scale parameter of the prior distribution, while IHBT is not. Using recommended default values for scaling parameters in NHBT leads to unpredictable operation characteristics, but selecting the scaling parameter to bias the Bayes factor towards the null hypothesis by 19 if the observed effect size is zero can address this issue in some cases. However, this does not solve all problems associated with NHBT.
PSYCHOLOGICAL METHODS
(2022)
Article
Economics
Xinxin Yang, Xinghua Zheng, Jiaqi Chen
Summary: Tests for high-dimensional covariance matrices based on a generalized elliptical model are developed, without assuming specific parametric distributions or involving data kurtosis. These tests can be used to test uncorrelatedness among idiosyncratic returns, demonstrating their flexibility and applicability.
JOURNAL OF ECONOMETRICS
(2021)
Article
Statistics & Probability
Deepak Nag Ayyala, Santu Ghosh, Daniel F. Linder
Summary: This paper proposes a test procedure called CRAMP for testing hypotheses involving one or more covariance matrices using random projections, which alleviates the curse of dimensionality in high dimensions. Through randomly projecting high-dimensional data into lower-dimensional subspaces, traditional multivariate tests can be used effectively.
COMPUTATIONAL STATISTICS
(2022)
Article
Multidisciplinary Sciences
Jose Jairo Santana-e-Silva, Francisco Cribari-Neto, Klaus L. P. Vasconcellos
Summary: The beta distribution is commonly used to model variables in (0, 1). This paper examines its adequacy and tests the hypothesis using information matrix equality. The results show that the beta distribution performs well in representing Covid-19 death rates, especially when considering the impact of vaccination.
Article
Computer Science, Interdisciplinary Applications
Joris Mulder, Donald R. Williams, Xin Gu, Andrew Tomarken, Florian Boing-Messing, Anton Olsson-Collentine, Marlyne Meijerink, Janosch Menke, Robbie van Aert, Jean-Paul Fox, Herbert Hoijtink, Yves Rosseel, Eric-Jan Wagenmakers, Caspar van Lissa
Summary: There have been significant methodological developments in Bayes factors for hypothesis testing in the social and behavioral sciences, but available software tools are still limited. BFpack is a new R package that offers Bayes factor hypothesis testing functions for common testing problems.
JOURNAL OF STATISTICAL SOFTWARE
(2021)
Article
Engineering, Electrical & Electronic
Ziwei Liu, Shanshan Zhao, Chen Zhang, Gengxin Zhang
Summary: The nonstationarity of interferences and array errors can significantly degrade the interference cancellation performance of an adaptive beamformer. Covariance matrix tapering (CMT) can create wide troughs in the receiving pattern and is a promising solution. However, the need for symmetrical and equivalent-width nulls in adaptive patterns is often unnecessary, as different interferences are unlikely to have the same nonstationarity. This paper proposes a flexible method for null widening that can produce wide null with different desired width and asymmetry, which is based on spatial asymmetrical interference cluster and different tapering matrices.
IEEE SENSORS JOURNAL
(2021)
Article
Statistics & Probability
Anestis Touloumis, John C. Marioni, Simon Tavare
Summary: The matrix-variate normal distribution is a popular model for high-dimensional transposable data. There is a proposed testing methodology for assessing the covariance matrices in high-dimensional settings, which shows robustness to normality departures and good statistical power against alternative hypotheses. The utility of the proposed tests is demonstrated through analysis of microarray and electroencephalogram studies.
Article
Statistics & Probability
Lijuan Huo, Jin Seo Cho
Summary: This study tests the sandwich-form asymptotic covariance matrices under conditionally heteroskedastic and/or autocorrelated regression errors, providing a testing methodology to detect their influence. A sequential testing procedure is established to achieve the research goal, confirming the theory through simulation and empirical data.
Article
Mathematical & Computational Biology
Sunwoo Han, Youyi Fong, Ying Huang
Summary: Testing the global null hypothesis that there are no significant predictors for a binary outcome of interest among a large set of biomarker measurements is crucial in biomedical studies. This paper proposes to enhance the power of such testing methods by utilizing ensemble machine learning techniques. The effectiveness of the proposed methods is demonstrated through Monte Carlo studies and the application to immunologic biomarkers dataset from the RV144 HIV vaccine efficacy trial.
STATISTICS IN MEDICINE
(2022)
Article
Health Care Sciences & Services
Richard McNulty
Summary: NHST's internal logic can be analyzed using propositional calculus, with the testable H-0 determined by analyzing the range of P-values; The correspondence between H-0 and H-A must be exhaustive to avoid false dichotomies; The conclusions derived from NHST only justify that the results are not due to chance alone, rather than proving the research hypothesis is true.
BMC MEDICAL RESEARCH METHODOLOGY
(2022)
Article
Mathematics, Applied
Xiaozhuo Zhang, Zhidong Bai, Jiang Hu
Summary: This paper investigates the limiting spectral distribution and analytic behavior of high-dimensional noncentral Fisher matrices and presents the determination criterion for the support of the limiting spectral distribution.
SCIENCE CHINA-MATHEMATICS
(2023)
Article
Biochemical Research Methods
Ting Wang, Haojie Lu, Ping Zeng
Summary: Pleiotropy is important for understanding the genetic connection between complex phenotypes and diseases. This study proposes a gene-based method called MAIUP for efficient pleiotropy identification, considering the high-dimensional composite null hypothesis. MAIUP takes into account the composite nature of pleiotropy test and provides well-calibrated P-values for controlling family-wise error rate and false discovery rate. It also effectively addresses the issue of overlapping subjects commonly encountered in association studies. Simulation studies demonstrate the superiority of MAIUP in maintaining correct type I error control and higher power compared to other methods. Application of MAIUP to psychiatric disorders discovers new pleiotropic genes and functional and enrichment analyses support their association with the disorders.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Statistics & Probability
T. Tony Cai, Weidong Liu, Harrison H. Zhou
ANNALS OF STATISTICS
(2016)
Article
Statistics & Probability
T. Tony Cai, Weidong Liu
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2016)
Article
Statistics & Probability
T. Tony Cai, Hongzhe Li, Weidong Liu, Jichun Xie
Article
Rheumatology
Katherine P. Liao, Jeffrey A. Sparks, Boris P. Hejblum, I-Hsin Kuo, Jing Cui, Lauren J. Lahey, Andrew Cagan, Vivian S. Gainer, Weidong Liu, T. Tony Cai, Jeremy Sokolove, Tianxi Cai
ARTHRITIS & RHEUMATOLOGY
(2017)
Article
Statistics & Probability
Xi Chen, Weidong Liu
ANNALS OF STATISTICS
(2018)
Article
Statistics & Probability
Hongyuan Cao, Weidong Liu, Zhou Zhou
Article
Statistics & Probability
Degui Li, Weidong Liu, Qiying Wang, Wei Biao Wu
Article
Statistics & Probability
Xi Chen, Weidong Liu
Article
Statistics & Probability
Xi Chen, Weidong Liu, Yichen Zhang
ANNALS OF STATISTICS
(2019)
Article
Statistics & Probability
Xi Chen, Weidong Liu, Yichen Zhang
Summary: This article investigates distributed estimation and inference for a general statistical problem by proposing a new multi-round distributed estimation procedure to overcome restrictions on the number of machines. The method introduces a computationally efficient estimator for Sigma(-1)w, applicable to nondifferentiable losses, facilitating inference for the empirical risk minimizer.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2022)
Article
Computer Science, Artificial Intelligence
Jiyuan Tu, Weidong Liu, Xiaojun Mao
Summary: This paper presents a Byzantine-resilient method for distributed sparse M-estimation. By constructing a pseudo-response variable and transforming the optimization problem, a communication-efficient distributed algorithm is developed. Theoretically, it is proven that the proposed method achieves fast convergence and a support recovery result is established.
Article
Automation & Control Systems
Xi Chen, Weidong Liu, Xiaojun Mao, Zhuoyi Yang
JOURNAL OF MACHINE LEARNING RESEARCH
(2020)
Article
Statistics & Probability
Mark Fiecas, Chenlei Leng, Weidong Liu, Yi Yu
ELECTRONIC JOURNAL OF STATISTICS
(2019)
Article
Statistics & Probability
Tianxi Cai, T. Tony Cai, Katherine Liao, Weidong Liu
Article
Statistics & Probability
Weidong Liu
ANNALS OF STATISTICS
(2017)