
Test of Significance for Highdimensional Thresholds with Application to Individualized Minimal Clinically Important Difference
This work is motivated by learning the individualized minimal clinically...
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Optimal Covariate Balancing Conditions in Propensity Score Estimation
Inverse probability of treatment weighting (IPTW) is a popular method fo...
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On the global identifiability of logistic regression models with misclassified outcomes
In the last decade, the secondary use of large data from health systems,...
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Exponential Family Graphical Models: Correlated Replicates and Unmeasured Confounders, with Applications to fMRI Data
Graphical models have been used extensively for modeling brain connectiv...
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Optimal Semisupervised Estimation and Inference for Highdimensional Linear Regression
There are many scenarios such as the electronic health records where the...
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Doubly Robust Semiparametric DifferenceinDifferences Estimators with HighDimensional Data
This paper proposes a doubly robust twostage semiparametric difference...
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Sparse Confidence Sets for Normal Mean Models
In this paper, we propose a new framework to construct confidence sets f...
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Estimation and inference on highdimensional individualized treatment rule in observational data using splitandpooled decorrelated score
With the increasing adoption of electronic health records, there is an i...
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Adaptive Estimation of Multivariate Regression with Hidden Variables
This paper studies the estimation of the coefficient matrix in multivar...
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Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness
Recently smoothing deep neural network based classifiers via isotropic G...
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Heterogeneityaware and communicationefficient distributed statistical inference
In multicenter research, individuallevel data are often protected again...
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Regression Discontinuity Design under Selfselection
In Regression Discontinuity (RD) design, selfselection leads to differe...
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Optimal Sampling for Generalized Linear Models under Measurement Constraints
Suppose we are using a generalized linear model to predict a scalar outc...
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Nonregular and Minimax Estimation of Individualized Thresholds in High Dimension with Binary Responses
Given a large number of covariates Z, we consider the estimation of a hi...
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Efficient augmentation and relaxation learning for individualized treatment rules using observational data
Individualized treatment rules aim to identify if, when, which, and to w...
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Robust Estimation of Causal Effects via HighDimensional Covariate Balancing Propensity Score
In this paper, we propose a robust method to estimate the average treatm...
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HighDimensional Inference for ClusterBased Graphical Models
Motivated by modern applications in which one constructs graphical model...
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Sparse Latent Factor Models with Pure Variables for Overlapping Clustering
The problem of overlapping variable clustering, ubiquitous in data scien...
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A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equations
We propose a new inferential framework for constructing confidence regio...
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Local and Global Inference for High Dimensional Nonparanormal Graphical Models
This paper proposes a unified framework to quantify local and global inf...
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A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models
We consider the problem of uncertainty assessment for low dimensional co...
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High Dimensional ExpectationMaximization Algorithm: Statistical Optimization and Asymptotic Normality
We provide a general theory of the expectationmaximization (EM) algorit...
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On Semiparametric Exponential Family Graphical Models
We propose a new class of semiparametric exponential family graphical mo...
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Testing and Confidence Intervals for High Dimensional Proportional Hazards Model
This paper proposes a decorrelationbased approach to test hypotheses an...
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A Likelihood Ratio Framework for High Dimensional Semiparametric Regression
We propose a likelihood ratio based inferential framework for high dimen...
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High Dimensional Semiparametric Latent Graphical Model for Mixed Data
Graphical models are commonly used tools for modeling multivariate rando...
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Yang Ning
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