Hao Hu

Works (2)

Updated: July 7th, 2023 21:16

2017 article

The robust EM-type algorithms for log-concave mixtures of regression models

Hu, H., Yao, W., & Wu, Y. (2017, February 3). Computational Statistics & Data Analysis.

By: H. Hu n, W. Yao * & Y. Wu n

author keywords: EM algorithm; Log-concave Maximum Likelihood Estimator; Mixture of regression model; Robust regression
topics (OpenAlex): Bayesian Methods and Mixture Models; Statistical Methods and Inference; Advanced Statistical Methods and Models
TL;DR: A new method is proposed to estimate the mixture regression parameters by only assuming that the components have log-concave error densities but the specific parametric family is unknown, which has comparable performance to the normal EM algorithm. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

2016 article

Maximum likelihood estimation of the mixture of log-concave densities

Hu, H., Wu, Y., & Yao, W. (2016, March 10). Computational Statistics & Data Analysis.

By: H. Hu n, Y. Wu n & W. Yao *

author keywords: Consistency; Log-concave maximum likelihood estimator (LCMLE); Mixture model
topics (OpenAlex): Bayesian Methods and Mixture Models; Statistical Methods and Inference; Statistical Distribution Estimation and Applications
TL;DR: This paper considers a much more flexible mixture model, which assumes each component density to be log-concave, and shows that the LCMLE improves the clustering results while comparing with the traditional MLE for parametric mixture models. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

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