An \(M\)-estimation-based procedure for determining the number of regression models in regression clustering

DOI10.1155/2007/37475OpenAlexW2045222793MaRDI QIDQ933900

Qing Shao, Yuehua Wu

Publication date: 28 July 2008

Published in: Journal of Applied Mathematics and Decision Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2007/37475

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

Partition of interval-valued observations using regression ⋮ Clusterwise functional linear regression models ⋮ Two-stage method based on local polynomial fitting for a linear heteroscedastic regression model and its application in economics ⋮ Unnamed Item

Uses Software

• Algorithm 39

Cites Work

• A maximum likelihood methodology for clusterwise linear regression
• A fast algorithm for clusterwise linear regression
• A strongly consistent information criterion for linear model selection based on \(M\)-estimation
• Robust regression: Asymptotics, conjectures and Monte Carlo
• A consistent procedure for determining the number of clusters in regression clustering
• Estimating Mixtures of Normal Distributions and Switching Regressions
• Clustering Objects Generated by Linear Regression Models
• Generalised information criteria in model selection
• Convex Analysis
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• Unnamed Item
• Unnamed Item

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