Inference for high-dimensional linear mixed-effects models: A quasi-likelihood approach [0.03%]
高维线性混合效应模型的似然推断方法
Sai Li,T Tony Cai,Hongzhe Li
Sai Li
Linear mixed-effects models are widely used in analyzing clustered or repeated measures data. We propose a quasi-likelihood approach for estimation and inference of the unknown parameters in linear mixed-effects models with high-dimensional...
Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems [0.03%]
超高维监督问题中基于协变量信息量的特征筛选方法
Debmalya Nandy,Francesca Chiaromonte,Runze Li
Debmalya Nandy
Contemporary high-throughput experimental and surveying techniques give rise to ultrahigh-dimensional supervised problems with sparse signals; that is, a limited number of observations (n), each with a very large number of covariates (p >> ...
Estimating and accounting for unobserved covariates in high-dimensional correlated data [0.03%]
高维相关数据中未观察协变量的估计与修正
Chris McKennan,Dan Nicolae
Chris McKennan
Many high dimensional and high-throughput biological datasets have complex sample correlation structures, which include longitudinal and multiple tissue data, as well as data with multiple treatment conditions or related individuals. These ...
Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression [0.03%]
使用干扰惩罚回归涉及高维数据的目标推理
Qiang Sun,Heping Zhang
Qiang Sun
Analysis of high dimensional data has received considerable and increasing attention in statistics. In practice, we may not be interested in every variable that is observed. Instead, often some of the variables are of particular interest, a...
Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models [0.03%]
高维逻辑回归模型的全局检验与同时检验
Rong Ma,T Tony Cai,Hongzhe Li
Rong Ma
High-dimensional logistic regression is widely used in analyzing data with binary outcomes. In this paper, global testing and large-scale multiple testing for the regression coefficients are considered in both single- and two-regression set...
Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data [0.03%]
高维高阶数据的最优稀疏奇异值分解
Anru Zhang,Rungang Han
Anru Zhang
In this article, we consider the sparse tensor singular value decomposition, which aims for dimension reduction on high-dimensional high-order data with certain sparsity structure. A method named sparse tensor alternating thresholding for s...
A Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis [0.03%]
一种用于高维相关数据分析的分布式矩法
Emily C Hector,Peter X-K Song
Emily C Hector
This paper is motivated by a regression analysis of electroencephalography (EEG) neuroimaging data with high-dimensional correlated responses with multi-level nested correlations. We develop a divide-and-conquer procedure implemented in a f...
D-CCA: A Decomposition-based Canonical Correlation Analysis for High-Dimensional Datasets [0.03%]
基于分解的高维数据集典范相关分析（D-CCA）方法研究
Hai Shu,Xiao Wang,Hongtu Zhu
Hai Shu
A typical approach to the joint analysis of two high-dimensional datasets is to decompose each data matrix into three parts: a low-rank common matrix that captures the shared information across datasets, a low-rank distinctive matrix that c...
MWPCR: Multiscale Weighted Principal Component Regression for High-dimensional Prediction [0.03%]
基于多尺度加权主成分回归的高维数据分析方法
Hongtu Zhu,Dan Shen,Xuewei Peng et al.
Hongtu Zhu et al.
We propose a multiscale weighted principal component regression (MWPCR) framework for the use of high dimensional features with strong spatial features (e.g., smoothness and correlation) to predict an outcome variable, such as disease statu...
Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening [0.03%]
成对Sure独立筛选的超高维多类线性判别分析方法研究
Rui Pan,Hansheng Wang,Runze Li
Rui Pan
This paper is concerned with the problem of feature screening for multi-class linear discriminant analysis under ultrahigh dimensional setting. We allow the number of classes to be relatively large. As a result, the total number of relevant...