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A Selective Overview of Semiparametric Mixture of Regression Models

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New Frontiers of Biostatistics and Bioinformatics

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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Abstract

Finite mixture of regression models have been popularly used in many applications. In this article, we did a systematic review of newly developed semiparametric mixture of regression models. Recent developments and some open questions are also discussed.

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Acknowledgements

Xiang’s research is supported by Zhejiang Provincial NSF of China [grant no. LQ16A010002] and NSF of China [grant no. 11601477]. Yao’s research is supported by NSF [grant no. DMS-1461677] and Department of Energy with the award DE-EE0007328.

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Authors and Affiliations

  1. School of Data Sciences, Zhejiang University of Finance & Economics, Hangzhou, China

    Sijia Xiang

  2. Department of Statistics, University of California, Riverside, CA, USA

    Weixin Yao

Authors
  1. Sijia Xiang
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  2. Weixin Yao
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Correspondence to Sijia Xiang .

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Editors and Affiliations

  1. Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA

    Yichuan Zhao

  2. Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Ding-Geng Chen

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Xiang, S., Yao, W. (2018). A Selective Overview of Semiparametric Mixture of Regression Models. In: Zhao, Y., Chen, DG. (eds) New Frontiers of Biostatistics and Bioinformatics. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-99389-8_2

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