Ao Yuan,Wenqing He,Binhuan Wang et al.
Ao Yuan et al.
In this paper we study U-statistics with side information incorporated using the method of empirical likelihood. Some basic properties of the proposed statistics are investigated. We find that by implementing the side information properly, ...
Adjusting for High-dimensional Covariates in Sparse Precision Matrix Estimation by ℓ1-Penalization [0.03%]
带高维协变量的稀疏精确矩阵估计的ℓ1惩罚调整方法研究
Jianxin Yin,Hongzhe Li
Jianxin Yin
Motivated by the analysis of genetical genomic data, we consider the problem of estimating high-dimensional sparse precision matrix adjusting for possibly a large number of covariates, where the covariates can affect the mean value of the r...
Bayesian modeling of the dependence in longitudinal data via partial autocorrelations and marginal variances [0.03%]
基于部分自相关和边缘方差的纵向数据依赖性建模(英)
Y Wang,M J Daniels
Y Wang
Many parameters and positive-definiteness are two major obstacles in estimating and modelling a correlation matrix for longitudinal data. In addition, when longitudinal data is incomplete, incorrectly modelling the correlation matrix often ...
Xin Qi,Ruiyan Luo,Hongyu Zhao
Xin Qi
Recent years have seen the developments of several methods for sparse principal component analysis due to its importance in the analysis of high dimensional data. Despite the demonstration of their usefulness in practical applications, they...
Simultaneous Multiple Response Regression and Inverse Covariance Matrix Estimation via Penalized Gaussian Maximum Likelihood [0.03%]
惩罚高斯最大似然的同时多重响应回归和逆协方差矩阵估计
Wonyul Lee,Yufeng Liu
Wonyul Lee
Multivariate regression is a common statistical tool for practical problems. Many multivariate regression techniques are designed for univariate response cases. For problems with multiple response variables available, one common approach is...
Abhishek Bhattacharya,David Dunson
Abhishek Bhattacharya
Our first focus is prediction of a categorical response variable using features that lie on a general manifold. For example, the manifold may correspond to the surface of a hypersphere. We propose a general kernel mixture model for the join...
Ao Yuan,Jinfeng Xu,Gang Zheng
Ao Yuan
It is known that in many missing data models, for example, survival data models, some parameters are root-n estimable while the others are not. When they are, their limiting distributions are often Gaussian and easy to use. When they are no...
Nonparametric estimation of multivariate scale mixtures of uniform densities [0.03%]
多元均匀密度尺度混合的非参数估计方法研究
Marios G Pavlides,Jon A Wellner
Marios G Pavlides
Suppose that U = (U(1), … , U(d)) has a Uniform ([0, 1](d)) distribution, that Y = (Y(1), … , Y(d)) has the distribution G on [Formula: see text], and let X = (X(1), … , X(d)) = (U(1)Y(1), … , U(d)Y(d)). The resulting class of distribut...
Principled sure independence screening for Cox models with ultra-high-dimensional covariates [0.03%]
分层原理 sure 筛选算法在Cox模型中的应用
Sihai Dave Zhao,Yi Li
Sihai Dave Zhao
It is rather challenging for current variable selectors to handle situations where the number of covariates under consideration is ultra-high. Consider a motivating clinical trial of the drug bortezomib for the treatment of multiple myeloma...
Jianxin Yin,Hongzhe Li
Jianxin Yin
Motivated by analysis of gene expression data measured over different tissues or over time, we consider matrix-valued random variable and matrix-normal distribution, where the precision matrices have a graphical interpretation for genes and...