Functional-Coefficient Quantile Regression for Panel Data with Latent Group S…
This article considers estimating functional-coefficient models in panel quantile regression with individual effects, allowing the cross-sectional and temporal dependence for large panel observatio…
Feature Screening for Ultrahigh Dimensional Categorical Data with Applications
Ultrahigh dimensional data with both categorical responses and categorical covariates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable…
Estimating Mixture of Gaussian Processes by Kernel Smoothing
When functional data are not homogenous, for example, when there are multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this article, we propose a new…
Varying Coefficient Mediation Model and Application to Analysis of Behavioral…
This paper is concerned with causal mediation analysis with varying indirect and direct effects. We propose a varying coefficient mediation model, which can also be viewed as an extension of modera…
Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces vi…
We consider the stochastic contextual bandit problem under the high dimensional linear model. We focus on the case where the action space is finite and random, with each action associated with a ra…
Projection Test for Mean Vector in High Dimensions
This article studies the projection test for high-dimensional mean vectors via optimal projection. The idea of projection test is to project high-dimensional data onto a space of low dimension such…
Model-Free Conditional Feature Screening with FDR Control
In this article, we propose a model-free conditional feature screening method with false discovery rate (FDR) control for ultra-high dimensional data. The proposed method is built upon a new measur…
Power-Enhanced Simultaneous Test of High-Dimensional Mean Vectors and Covaria…
Power-enhanced tests with high-dimensional data have received growing attention in theoretical and applied statistics in recent years. Existing tests possess their respective high-power regions, an…
Regularized Linear Programming Discriminant Rule with Folded Concave Penalty …
We propose the regularized linear programming discriminant (LPD) rule with folded concave penalty in the ultrahigh-dimensional regime. We use the local linear approximation (LLA) algorithm to redir…
Testing the Effects of High-Dimensional Covariates via Aggregating Cumulative…
In this article, we test for the effects of high-dimensional covariates on the response. In many applications, different components of covariates usually exhibit various levels of variation, which …
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