A weighted least-squares approach to clusterwise regression

DOI10.1007/s10182-011-0155-4zbMath1477.62163OpenAlexW2079879667MaRDI QIDQ2006895

Rainer Schlittgen

Publication date: 12 October 2020

Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10182-011-0155-4

zbMATH Keywords

clusteringfinite mixture modellinear regressionrobust regressionweighted regressionbootstrap testclusterwise regression

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05)

Related Items

Robust estimation of mixtures of regressions with random covariates, via trimming and constraints, Robust fitting of hidden Markov regression models under a longitudinal setting

Uses Software

• R
• flexmix
• fpc
• fields
• Algorithm 39
• bootlib

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• bootlib
• A maximum likelihood methodology for clusterwise linear regression
• Fitting finite mixtures of generalized linear regressions in \textsf{R}
• Algorithm: Clusterwise linear least absolute deviations regression
• A fast algorithm for clusterwise linear regression
• Clusterwise linear regression
• Clusters, outliers, and regression: Fixed point clusters
• Fixed point clusters for linear regression: Computation and comparison
• A mathematical programming approach to clusterwise regression model and its extensions
• Finite mixture and Markov switching models.
• Estimating the Propagation Rate of a Viral Infection of Potato Plants via Mixtures of Regressions