activity
20042019
most citedNonconcave penalized likelihood with a diverging number of parameters

1.1k citations · 1.5k across the 13 of their papers we have counts for

collaborators

13 papers

stat.ME2019

BOLT-SSI: A Statistical Approach to Screening Interaction Effects for Ultra-High Dimensional Data

Min Zhou, Mingwei Dai, Yuan Yao +3

Detecting interaction effects among predictors on the response variable is a crucial step in various applications. In this paper, we first propose a simple method for sure screenin…

stat.ME2018

Fast Inference Procedures for Semivarying Coefficient Models via Local Averaging

Heng Peng, Chuanlong Xie, Jingxin Zhao

The semivarying coefficient models are widely used in the application of finance, economics, medical science and many other areas. The functional coefficients are commonly estimate…

stat.AP2018★ 1 cited

BIVAS: A scalable Bayesian method for bi-level variable selection with applications

Mingxuan Cai, Mingwei Dai, Jingsi Ming +3

In this paper, we consider a Bayesian bi-level variable selection problem in high-dimensional regressions. In many practical situations, it is natural to assign group membership to…

stat.ME2018★ 3 cited

Varying Coefficient Panel Data Model with Interactive Fixed Effects

Sanying Feng, Gaorong Li, Heng Peng +1

In this paper, we propose a varying coefficient panel data model with unobservable multiple interactive fixed effects that are correlated with the regressors. We approximate each c…

stat.ME2017

Unsupervised Learning of Mixture Regression Models for Longitudinal Data

Peirong Xu, Heng Peng, Tao Huang

This paper is concerned with learning of mixture regression models for individuals that are measured repeatedly. The adjective "unsupervised" implies that the number of mixing comp…

stat.ME2014★ 1 cited

Estimation of Partially Linear Regression Model under Partial Consistency Property

Xia Cui, Ying Lu, Heng Peng

In this paper, utilizing recent theoretical results in high dimensional statistical modeling, we propose a model-free yet computationally simple approach to estimate the partially…