Publications (16)
Model-free Feature Screening via Revised Chatterjee's Rank Correlation for Ultra-high Dimensional Censored Data
Shuya Chen, Heng Peng, Min Zhou
In large-scale biomedical research, it's common to gather ultra-high dimensional data that includes right-censored survival times. Feature screening has emerged as a crucial statis…
Model Selection for Gaussian Mixture Models
Tao Huang, Heng Peng, Kun Zhang
This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for mode…
Nonconcave penalized likelihood with a diverging number of parameters
Jianqing Fan, Heng Peng
A class of variable selection procedures for parametric models via nonconcave penalized likelihood was proposed by Fan and Li to simultaneously estimate parameters and select impor…
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…
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…
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…
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…
Nonconcave Penalized Spline
Heng Peng
Regression spline is a useful tool in nonparametric regression. However, finding the optimal knot locations is a known difficult problem. In this article, we introduce the Non-conc…
A Two-Step Projection-Based Goodness-of-Fit Test for Ultra-High Dimensional Sparse Regressions
Falong Tan, Jie Liu, Heng Peng +1
This paper proposes a novel two-step strategy for testing the goodness-of-fit of parametric regression models in ultra-high dimensional sparse settings, where the predictor dimensi…
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…
Model Free Prediction with Uncertainty Assessment
Yuling Jiao, Lican Kang, Jin Liu +2
Deep nonparametric regression, characterized by the utilization of deep neural networks to learn target functions, has emerged as a focus of research attention in recent years. Des…
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…
Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data
Ming-Yen Cheng, Toshio Honda, Jialiang Li +1
Ultra-high dimensional longitudinal data are increasingly common and the analysis is challenging both theoretically and methodologically. We offer a new automatic procedure for fin…
Robust rank correlation based screening
Gaorong Li, Heng Peng, Jun Zhang +1
Independence screening is a variable selection method that uses a ranking criterion to select significant variables, particularly for statistical models with nonpolynomial dimensio…
Bridging Information Criteria and Parameter Shrinkage for Model Selection
Kun Zhang, Heng Peng, Laiwan Chan +1
Model selection based on classical information criteria, such as BIC, is generally computationally demanding, but its properties are well studied. On the other hand, model selectio…
Component Selection in the Additive Regression Model
Xia Cui, Heng Peng, Songqiao Wen +1
Similar to variable selection in the linear regression model, selecting significant components in the popular additive regression model is of great interest. However, such componen…