Semiparametric mixtures of nonparametric regressions
type: Item
instance of: scholarly article
published in: Annals of the Institute of Statistical Mathematics
volume: 70
issue: 1
page(s): 131-154
publication date: 2016-11-05T00:00:00Z
publication date: 2018-02-08T00:00:00Z
author: Sijia Xiang
author: Weixin Yao
title: Semiparametric mixtures of nonparametric regressions
zbMATH Open document ID: 1385.62014
review text: The authors propose a new class of semiparametric mixtures of regression models. Regression spline estimates are considered and the authors claim their simplicity. The proportions and variances remain constants and the component regression functions are considered as smooth functions of a certain covariate. Its identifiability is determined and the proposed estimation procedure seems to have an optimal convergence rate for both the global parameters and the nonparametric regression functions.
reviewed by: Carlos Narciso Bouza Herrera
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62G20
Mathematics Subject Classification ID: 62H30
zbMATH DE Number: 6836002
zbMATH Keywords: EM algorithm
zbMATH Keywords: kernel regression
zbMATH Keywords: mixture of regression models
zbMATH Keywords: semiparametric mixture models
describes a project that uses: ElemStatLearn
MaRDI profile type: Publication
full work available at URL: s10463-016-0584-7
OpenAlex ID: W2548866223
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DOI: 10.1007/S10463-016-0584-7
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description: scientific article; zbMATH DE number 6836002