Semiparametric mixtures of regressions with single-index for model based clustering |
| |
Authors: | Xiang Sijia Yao Weixin |
| |
Institution: | 1.School of Data Sciences, Zhejiang University of Finance & Economics, Hangzhou, Zhejiang, 310018, People’s Republic of China ;2.Department of Statistics, University of California, Riverside, CA, USA ; |
| |
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: | |
本文献已被 SpringerLink 等数据库收录! |
|