CARS:Covariate-Assisted Two-Sample Inference]CARS:Covariate Assisted Ranking and Screening for Large

来源 :上海交通大学 | 被引量 : 0次 | 上传用户：caoyi1014

【摘要】

：

【作者】

：

Wenguang Sun T.Tony Cai Weinan Wang

【机构】

：

University of Southern California,Los Angeles,U.S.A.

【出处】

：

上海交通大学

【发表日期】

：

2016年4期

【关键词】

：

Compound decision theory False discovery rate Logically correlated tests Multipl

下载到本地 , 更方便阅读

下载此文赞助VIP

声明 : 本文档内容版权归属内容提供方 , 如果您对本文有版权争议 , 可与客服联系进行内容授权或下架

论文部分内容阅读

　　Two-sample multiple testing has a wide range of applications.The conventional practice is to first reduce the original observations to a vector of p-values and then choose a cutoff to adjust for multiplicity.However,the data reduction step could cause significant loss of information and thus lead to suboptimal testing procedures.

其他文献

Generalized Linear Mixed Models with Gaussian Mixture Random Effects

会议

Estimate Variable Importance for Recurrent Event Outcomes with an Application to Identify Hypoglycem

Recurrent event data are important data type for medical research.In particular,many safety endpoints are recurrent outcomes,such as hypoglycemic events.

会议

Gradient BoostingRecurrent Event DataRandom ForestVariable ImportanceVariabl

A HGLM Modeling Approach for Accelerated Life Tests with Random Effects

Most analyses of accelerated life test(ALT)ignore random effects even if information is available to estimate these effects because of difficulties in the traditional frequentist analysis methods.Igno

会议

Accelerated life testh-likelihoodrandom effects

Frailty Modelling Approaches for Semi-competing Risks Data

We consider the semi-competing risks situation where a terminal event(e.g.death)censors a non-terminal event(e.g.disease recurrence),but not vice versa.

会议

Competing risksFrailty modelsH-likelihoodMarginal likelihoodSemi-competing r

Estimating Modular Brain Connectivity from High-Dimensional fMRI Data using a Multi-Scale Factor Mod

We consider the challenges in modeling and estimating large-scale functional brain connectivity with a hierarchical and modular structure from high-dimensional functional magnetic resonance imaging(fM

会议

Factor analysisprincipal component analysishigh-dimensional covariancebrain c

Tractable Non-Convexity:Matrix Completion,Saddlepoints,and Gradient Descent

Matrix completion is a fundamental machine learning problem with wide applications in collaborative filtering and recommender systems.

会议

Matrix Completionnon-convexGradient descent

Endpoint estimation for observations with normal measurement errors

Let {Xi}ni =1 be i.i.d.random variables with a common distribution function F.Suppose that F belongs to the Weibull domain of attraction with a finite endpoint θ.

会议

Weibull domain of attractionorder statisticstail quantile process

A Factor-Adjusted Multiple Testing Procedure with Application to Mutual Fund Selection

In this talk,we propose a factor-adjusted multiple testing(FAT)procedure based on factor-adjusted p-values in a linear factor model involving some observable and unobservable factors,for the purpose o

会议

Factor ModelFalse Discovery ProportionFund PerformanceHigh Dimensional Data

DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding

Transcription factors(TFs)bind to specific DNA sequence motifs.Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor gr

会议

TF-DNA binding specificityhigh-throughput binding assayskernel methodssupport

Large Deviation Probabilities for Sums of Heavy-Tailed Random Vectors

Let {Xi,i≥ 1} be i.i.d.Rd-valued random vectors attracted to operator semi-stable laws and write Sn =∑n i=1 Xi.This paper investigates precise large deviations for both the partial sums Sn and the ran

会议

CARS:Covariate-Assisted Two-Sample Inference]CARS:Covariate Assisted Ranking and Screening for Large

其他学术论文