Semiparametric mixtures of regressions with single-index for model based clustering

Sijia Xiang () and Weixin Yao ()
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Sijia Xiang: Zhejiang University of Finance & Economics
Weixin Yao: University of California

Advances in Data Analysis and Classification, 2020, vol. 14, issue 2, No 3, 292 pages

Abstract: Abstract In this article, we propose two classes of semiparametric mixture regression models with single-index for model based clustering. Unlike many semiparametric/nonparametric mixture regression models that can only be applied to low dimensional predictors, the new semiparametric models can easily incorporate high dimensional predictors into the nonparametric components. The proposed models are very general, and many of the recently proposed semiparametric/nonparametric mixture regression models are indeed special cases of the new models. Backfitting estimates and the corresponding modified EM algorithms are proposed to achieve optimal convergence rates for both parametric and nonparametric parts. We establish the identifiability results of the proposed two models and investigate the asymptotic properties of the proposed estimation procedures. Simulation studies are conducted to demonstrate the finite sample performance of the proposed models. Two real data applications using the new models reveal some interesting findings.

Keywords: EM algorithm; Kernel regression; Mixture regression model; Model based clustering; Single-index model; 62G08; 62E20 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11634-020-00392-w

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