Merging Components in Linear Gaussian Cluster-Weighted Models

Sangkon Oh and Byungtae Seo ()
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Sangkon Oh: Sungkyunkwan University
Byungtae Seo: Sungkyunkwan University

Journal of Classification, 2023, vol. 40, issue 1, No 3, 25-51

Abstract: Abstract Cluster-weighted models (CWMs) are useful tools for identifying latent functional relationships between response variables and covariates. However, owing to excess distributional assumptions made on the covariates, these models can suffer misspecifications of component distributions, which could also undermine the estimation accuracy and render the model structure complicated for interpretation. To address this issue, we consider CWMs with univariate responses and propose a novel CWM by modelling each cluster as a finite mixture to enhance flexibility while retaining parsimony. We prove that the proposed method can provide more meaningful clusters in the data than those of existing methods. Additionally, we present a procedure to construct such a proposed CWM and a feasible expectation-maximization algorithm to estimate the model parameters. Numerical demonstrations, including simulations and real data analysis, are also provided.

Keywords: Mixture models; Cluster-weighted models; Model-based clustering; Machine learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00357-022-09424-w

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