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20042019
most citedModel Selection for Gaussian Mixture Models

27 citations · 29 across the 3 of their papers we have counts for

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6 papers · 1 filter

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.ME2018

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.ME201327 cited

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…

stat.ME20121 cited

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…

stat.ME20101 cited

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…