An \(M\)-estimation-based procedure for determining the number of regression models in regression clustering
type: Item
instance of: scholarly article
title: An \(M\)-estimation-based procedure for determining the number of regression models in regression clustering
DOI: 10.1155/2007/37475
author: Qing Shao
author: Yuehua Wu
author: C. R. Rao
published in: Journal of Applied Mathematics and Decision Sciences
publication date: 2008-07-28T00:00:00Z
review text: Summary: In this paper, a procedure based on \(M\)-estimation to determine the number of regression models for the problem of regression clustering is proposed. We have shown that the true classification is attained when \(n\) increases to infinity under certain mild conditions, for instance, without assuming normality of the distribution of the random errors in each regression model.
Mathematics Subject Classification ID: 62-XX
zbMATH DE Number: 5304406
describes a project that uses: Algorithm 39
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2007/37475
OpenAlex ID: W2045222793
cites work: Q4373484
cites work: Estimating Mixtures of Normal Distributions and Switching Regressions
cites work: A fast algorithm for clusterwise linear regression
cites work: A maximum likelihood methodology for clusterwise linear regression
cites work: Clustering Objects Generated by Linear Regression Models
cites work: A consistent procedure for determining the number of clusters in regression clustering
cites work: Q3723487
cites work: Robust regression: Asymptotics, conjectures and Monte Carlo
cites work: Generalised information criteria in model selection
cites work: A strongly consistent information criterion for linear model selection based on \(M\)-estimation
cites work: Convex Analysis
cites work: Q4195812
description: scientific article; zbMATH DE number 5304406