Learning Human Activity Patterns Using Clustered Point Processes With Active …

Modeling event patterns is a central task in a wide range of disciplines. In applications such as studying human activity patterns, events often arrive clustered with sporadic and long periods of i…

Feature Screening for Massive Data Analysis by Subsampling

Modern statistical analysis often encounters massive datasets with ultrahigh-dimensional features. In this work, we develop a subsampling approach for feature screening with massive datasets. The a…

A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Upda…

Quantile regression is a method of fundamental importance. How to efficiently conduct quantile regression for a large dataset on a distributed system is of great importance. We show that the popula…

Autoregressive Model With Spatial Dependence and Missing Data

We study herein an autoregressive model with spatially correlated error terms and missing data. A logistic regression model with completely observed covariates is used to model the missingness mech…

Least-Square Approximation for a Distributed System

In this work, we develop a distributed least-square approximation (DLSA) method that is able to solve a large family of regression problems (e.g., linear regression, logistic regression, and Cox’…

Sequential Text-Term Selection in Vector Space Models

Text mining has recently attracted a great deal of attention with the accumulation of text documents in all fields. In this article, we focus on the use of textual information to explain continuous…

Forward regression for ultra-high dimensional variable screening

Robust regression shrinkage and consistent variable selection through the LAD…

Testing the Diagonality of a Large Covariance Matrix in a Regression Setting

Feature Screening for Ultrahigh Dimensional Categorical Data with Applications