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FDR Control for Pairwise Comparisons of Normal Means Based on Goodness of Fit Test

Acta Mathematicae Applicatae Sinica 2016年第3期32卷 701-712页

作者： Pei JIN Xing-zhong XU Na LI School of Mathematics and Statistics Beijing Institute of Technology School of Economics and Management University of the Chinese Academy of Sciences

This study is undertaken to apply a bootstrap method of controlling the false discovery rate （FDR） when performing pairwise comparisons of normal means. Due to the dependency of test statistics in pairwise comparisons, many conventional multiple testing procedures can＇t be employed directly. Some modified pro- cedures that control FDR with dependent test statistics are too conservative. In the paper, by bootstrap and goodness-of-fit methods, we produce independent p-values for pairwise comparisons. Based on these indepen- dent p-values, plenty of procedures can be used, and two typical FDR controlling procedures are applied here. An example is provided to illustrate the proposed approach. Extensive simulations show the satisfactory FDR control and power performance of our approach. In addition, the proposed approach can be easily extended to more than two normal, or non-normal, balance or unbalance cases.

关键词： multiple testing false discovery rate goodness-of-fit test pairwise comparisons p-value resampling

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Null-free False Discovery Rate Control Using Decoy Permutations

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Acta Mathematicae Applicatae Sinica 2022年第2期38卷 235-253页

作者： Kun He Mengjie Li Yan Fu Fuzhou Gong Xiaoming Sun Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China CEMS NCMISRCSDSAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

The traditional approaches to false discovery rate(FDR)control in multiple hypothesis testing are usually based on the null distribution of a test ***,all types of null distributions,including the theoretical,permutation-based and empirical ones,have some inherent *** example,the theoretical null might fail because of improper assumptions on the sample ***,we propose a null distributionfree approach to FDR control for multiple hypothesis testing in the case-control *** approach,named target-decoy procedure,simply builds on the ordering of tests by some statistic or score,the null distribution of which is not required to be *** decoy tests are constructed from permutations of original samples and are used to estimate the false target *** prove that this approach controls the FDR when the score function is symmetric and the scores are independent between different *** demonstrates that it is more stable and powerful than two popular traditional approaches,even in the existence of *** is also made on two real datasets,including an arabidopsis genomics dataset and a COVID-19 proteomics dataset.

关键词： multiple testing false discovery rate null distribution-free p-value-free decoy permutations knockoff filter

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Variable Selection in High-Dimensional Error-in-Variables Models via Controlling the False Discovery Proportion

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Communications in Mathematics and Statistics 2022年第1期10卷 123-151页

作者： Xudong Huang Nana Bao Kai Xu Guanpeng Wang School of Mathematics and Statistics Anhui Normal University189 South Jiuhua RoadWuhu 241002AnhuiPeople’s Republic of China School of Mathematical Sciences Capital Normal University105 West Third Ring RoadBeijing 100048People’s Republic of China

multiple testing has gained much attention in high-dimensional statistical theory and applications,and the problem of variable selection can be regarded as a generalization of the multiple *** is aiming to select the important variables among many *** variable selection in high-dimensional linear models with measurement errors is *** the influence of high-dimensional parameters and measurement errors need to be considered to avoid severely *** consider the problem of variable selection in error-in-variables and introduce the DCoCoLasso-FDP procedure,a new variable selection *** constructing the consistent estimator of false discovery proportion(FDP)and false discovery rate(FDR),our method can prioritize the important variables and control FDP and FDR at a specifical level in error-in-variables *** extensive simulation study is conducted to compare DCoCoLasso-FDP procedure with existing methods in various settings,and numerical results are provided to present the efficiency of our method.

关键词： multiple testing High-dimensional inference False discovery proportion Measurement error models Variable selection

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maging genetics -towards discovery neuroscience

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Frontiers of Electrical and Electronic Engineering in China 2013年第4期8卷 227-245页

作者： Tian Ge Gunter Schumann Jianfeng Feng Centre for Computational Systems Biology School of Mathematical Sciences Fudan University Shanghai 200433 China Department of Computer Science University of Warwick Coventry CV4 7AL UK Institute of Psychiatry King's College London London WC2R 2LS UK

Imaging genetics is an emerging field aimed at identifying and characterizing genetic variants that influence measures derived from anatomical or functional brain images, which are in turn related to brain-related illnesses or fundamental cognitive, emotional and behavioral processes, and are affected by environmental factors. Here we review the recent evolution of statistical approaches and outstanding challenges in imaging genetics, with a focus on population-based imaging genetic association studies. We show the trend in imaging genetics from candidate approaches to pure discovery science, and from univariate to multivariate analyses. We also discuss future directions and prospects of imaging genetics for ultimately helping understand the genetic and environmental underpinnings of various neuropsychiatric disorders and turning basic science into clinical strategies.

关键词： imaging genetics association study multiple testing 5-O approach

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The influence of misspecified covariance on false discovery control when usingposterior probabilities

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Statistical Theory and Related Fields 2017年第2期1卷 205-215页

作者： Ye Liang Joshua D.Habiger Xiaoyi Min Department of Statistics Oklahoma State UniversityStillwaterOKUSA Department of Mathematics and Statistics Georgia State UniversityAtlantaGAUSA

This paper focuses on the influence of a misspecified covariance structure on false discoveryrate for the large-scale multiple testing ***,we evaluate the influence on themarginal distribution of local false discovery rate statistics,which are used in many multiple testing procedures and related to Bayesian posterior *** forms of the marginaldistributions under both correctly specified and incorrectly specified models are ***–Leibler divergence is used to quantify the influence caused by a *** numerical examples are provided to illustrate the influence.A real spatio-temporal data onsoil humidity is discussed.

关键词： multiple testing Bayes dependent data div

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基于控制过度遗漏发现概率的高维数据流异常诊断

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统计与精算 2020年第5期

作者：杨梓樱濮晓龙徐嘉辉

随着科技的创新和社会的进步，数据采集技术得到显著提升，高维数据流（High-dimensional Data Stream，HDS）在医学、机械、工业工程等领域开始广泛出现，除了HDS的在线监控之外，精确而高效的故障诊断也变得越来越重要。在本文中，我... 详细信息

随着科技的创新和社会的进步，数据采集技术得到显著提升，高维数据流（High-dimensional Data Stream，HDS）在医学、机械、工业工程等领域开始广泛出现，除了HDS的在线监控之外，精确而高效的故障诊断也变得越来越重要。在本文中，我们将HDS的故障诊断问题阐述为一个新颖的多重检验问题，并通过控制过度遗漏发现概率（Missed Discovery Excessive Probability，MDX）来对HDS进行异常诊断，克服了传统诊断方法的限制，并能够显著的提高异常诊断的稳健性和精确度。我们给出了MDX的Monte-Carlo近似计算方法，并在此基础上提出了Oracle和Data-Driven诊断程序。我们通过模拟研究和一个实例分析来阐明所提方法的优越特性。

关键词：故障诊断高维数据流统计过程控制过度遗漏发现概率多重检验 fault diagnosis high-dimensional data streams statistical process control missed discovery excessive probability multiple testing

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