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中国人民大学统计学院李扬教授及学生杨昊宇、俞昊辰就缺失数据变量选择问题在《Annals of Applied Statistics》发表论文（图））
李扬 数据分析 实验设计方法
2024/11/26
目前，针对广义线性模型下的缺失数据变量选择问题仍面临诸多挑战。本文通过广义估计方程将多重插补数据集间的相关性纳入至带有惩罚的变量选择模型，提出了基于多重插补数据的变量选择方法（PEE-MI）。本文证明了该方法具备变量选择的一致性和有效性，数值模拟和实证分析显示出该方法相较于已有方法具有优良的表现。
A simple variance inequality for U-statistics of a Markov chain with applications
U-statistics Markov chains Inequalities Limit theorems
2011/7/19
We establish a simple variance inequality for U-statistics whose underlying sequence of random variables is an ergodic Markov Chain.
Homogeneity and change-point detection tests for multivariate data using rank statistics
Homogeneity change-point detection tests multivariate data
2011/7/19
Detecting and locating changes in highly multivariate data is a major concern in several current statistical applications. In this context, the first contribution of the paper is a novel non-parametri...
Missing Data Imputation and Corrected Statistics for Large-Scale Behavioral Databases
missing data imputation statistics corrected for missing data item performance behavioral databases model goodness of fit
2011/3/23
This paper presents a new methodology to solve problems resulting from missing data in large-scale item performance behavioral databases. Useful statistics corrected for missing data are described, an...
Compressible Priors for High-dimensional Statistics
linear inverse problem LASSO sparsity sparse regression ridge regression com-pressible prior compressive sensing instance optimality maximum a posteriori high-dimensional statistics order statistics
2011/3/18
We develop a principled way of identifying probability distributions whose independent and identically distributed (iid) realizations are compressible, i.e., can be approximated as sparse. We focus on...
Large and Moderate Deviations for Hotelling's T^2-Statistics
random variable deviation normal law Hotelling's T^2-Statistics
2009/4/1
Let X , X1, X 2 , ... be i.i.d. R d-valued random variables. We prove large and moderate deviations for Hotelling's T2-statistic when X is in the generalized domain of attraction of the normal law.