Optimal detection of weak positive latent dependence between two sequences of multiple tests [0.03%]
两组多重假设检验间弱正隐含相依性的最优检测
Sihai Dave Zhao,T Tony Cai,Hongzhe Li
Sihai Dave Zhao
It is frequently of interest to jointly analyze two paired sequences of multiple tests. This paper studies the problem of detecting whether there are more pairs of tests that are significant in both sequences than would be expected by chanc...
Gyuhyeong Goh,Dipak K Dey,Kun Chen
Gyuhyeong Goh
Many modern statistical problems can be cast in the framework of multivariate regression, where the main task is to make statistical inference for a possibly sparse and low-rank coefficient matrix. The low-rank structure in the coefficient ...
A Statistical Framework for Pathway and Gene Identification from Integrative Analysis [0.03%]
一种用于从综合分析中识别途径和基因的统计框架
Quefeng Li,Menggang Yu,Sijian Wang
Quefeng Li
In the era of big data, integrative analyses that pool data from different sources are now extensively conducted in order to improve performance. Among many interesting applications, genomics research is an area where integrative methods be...
Min Tang,Eric V Slud,Ruth M Pfeiffer
Min Tang
Linear mixed models (LMMs) are widely used for regression analysis of data that are assumed to be clustered or correlated. Assessing model fit is important for valid inference but to date no confirmatory tests are available to assess the ad...
Jichun Xie,Jian Kang
Jichun Xie
Exploring resting-state brain functional connectivity of autism spectrum disorders (ASD) using functional magnetic resonance imaging (fMRI) data has become a popular topic over the past few years. The data in a standard brain template consi...
Minimax Rate-optimal Estimation of High-dimensional Covariance Matrices with Incomplete Data [0.03%]
高维协方差矩阵的最优估计及其在不完全观测数据下的估计方法研究
T Tony Cai,Anru Zhang
T Tony Cai
Missing data occur frequently in a wide range of applications. In this paper, we consider estimation of high-dimensional covariance matrices in the presence of missing observations under a general missing completely at random model in the s...
A Semi-Infinite Programming based algorithm for determining T-optimum designs for model discrimination [0.03%]
基于半无限规划的T-最优设计确定算法用于模型歧视
Belmiro P M Duarte,Weng Kee Wong,Anthony C Atkinson
Belmiro P M Duarte
T-optimum designs for model discrimination are notoriously difficult to find because of the computational difficulty involved in solving an optimization problem that involves two layers of optimization. Only a handful of analytical T-optima...
Bayesian regression analysis of data with random effects covariates from nonlinear longitudinal measurements [0.03%]
具有随机效应协变量的非线性纵向数据的贝叶斯回归分析
Rolando De la Cruz,Cristian Meza,Ana Arribas-Gil et al.
Rolando De la Cruz et al.
Joint models for a wide class of response variables and longitudinal measurements consist on a mixed-effects model to fit longitudinal trajectories whose random effects enter as covariates in a generalized linear model for the primary respo...
High-Dimensional Multivariate Repeated Measures Analysis with Unequal Covariance Matrices [0.03%]
协方差矩阵不等的高维多元重复测量分析
Solomon W Harrar,Xiaoli Kong
Solomon W Harrar
In this paper, test statistics for repeated measures design are introduced when the dimension is large. By large dimension is meant the number of repeated measures and the total sample size grow together but either one could be larger than ...
High dimensional data analysis using multivariate generalized spatial quantiles [0.03%]
基于多元广义空间分位数的高维数据分析
Nitai D Mukhopadhyay,Snigdhansu Chatterjee
Nitai D Mukhopadhyay
High dimensional data routinely arises in image analysis, genetic experiments, network analysis, and various other research areas. Many such datasets do not correspond to well-studied probability distributions, and in several applications t...