In many scenarios such as genome-wide association studies where dependen...
The estimation of the treatment effect is often biased in the presence o...
This paper studies the inference of the regression coefficient matrix un...
Matrix valued data has become increasingly prevalent in many application...
This work is motivated by learning the individualized minimal clinically...
Inverse probability of treatment weighting (IPTW) is a popular method fo...
In the last decade, the secondary use of large data from health systems,...
Graphical models have been used extensively for modeling brain connectiv...
There are many scenarios such as the electronic health records where the...
This paper proposes a doubly robust two-stage semiparametric
difference-...
In this paper, we propose a new framework to construct confidence sets f...
With the increasing adoption of electronic health records, there is an
i...
This paper studies the estimation of the coefficient matrix in
multivar...
Recently smoothing deep neural network based classifiers via isotropic
G...
In multicenter research, individual-level data are often protected again...
In Regression Discontinuity (RD) design, self-selection leads to differe...
Suppose we are using a generalized linear model to predict a scalar outc...
Given a large number of covariates Z, we consider the estimation of a
hi...
Individualized treatment rules aim to identify if, when, which, and to w...
In this paper, we propose a robust method to estimate the average treatm...
Motivated by modern applications in which one constructs graphical model...
The problem of overlapping variable clustering, ubiquitous in data scien...
We propose a new inferential framework for constructing confidence regio...
This paper proposes a unified framework to quantify local and global
inf...
We consider the problem of uncertainty assessment for low dimensional
co...
We provide a general theory of the expectation-maximization (EM) algorit...
We propose a new class of semiparametric exponential family graphical mo...
This paper proposes a decorrelation-based approach to test hypotheses an...
We propose a likelihood ratio based inferential framework for high
dimen...
Graphical models are commonly used tools for modeling multivariate rando...