10 Papers
20 Citations
Chun Yu is an academic researcher from Jiangxi University of Finance and Economics. The author has contributed to research in topics: Outlier & Expectation–maximization algorithm. The author has an hindex of 4, co-authored 10 publications. Previous affiliations of Chun Yu include Kansas State University.
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Papers
Robust linear regression: A review and comparison
Chun Yu,Weixin Yao +1 more
TL;DR: This article aims to review and describe some available and popular robust techniques, including some recent developed ones, and compare them in terms of breakdown point and efficiency.
Outlier detection and robust mixture modeling using nonconvex penalized likelihood
Chun Yu,Kun Chen,Weixin Yao +2 more
TL;DR: A robust mixture modeling approach is proposed using a mean-shift formulation coupled with nonconvex sparsity-inducing penalization, to conduct simultaneous outlier detection and robust parameter estimation, and an efficient iterative thresholding-embedded EM algorithm is developed to maximize the penalized log-likelihood.
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A Selective Overview and Comparison of Robust Mixture Regression Estimators
Chun Yu,Weixin Yao,Guangren Yang +2 more
TL;DR: In this article, a selective overview of the recently proposed robust mixture regression methods and compare their performance using simulation studies is presented, and a comparison of their performance with the normality-based maximum likelihood estimation is provided.
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A new class of multivariate goodness of fit tests for multivariate normal mixtures
TL;DR: The goodness of fit (GOF) test plays an important role to determine whether a data set comes from an assumed distribution (with possibly unknown parameters). Many approaches have been proposed to p...
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Pursuing sources of heterogeneity in modeling clustered population.
TL;DR: In this article, a regularized finite mixture effects regression was proposed to achieve heterogeneity pursuit and feature selection simultaneously, which can achieve both estimation and selection consistency in a heterogeneous population with mixed regression relationships.
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