Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4945180 | International Journal of Approximate Reasoning | 2017 | 19 Pages |
Abstract
Constrained approaches to maximum likelihood estimation in the context of finite mixtures of normals have been presented in the literature. A fully data-dependent soft constrained method for maximum likelihood estimation of clusterwise linear regression is proposed, which extends previous work in equivariant data-driven estimation of finite mixtures of normals. The method imposes soft scale bounds based on the homoscedastic variance and a cross-validated tuning parameter c. In our simulation studies and real data examples we show that the selected c will produce an output model with clusterwise linear regressions and clustering as a most-suited-to-the-data solution in between the homoscedastic and the heteroscedastic models.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Roberto Di Mari, Roberto Rocci, Stefano Antonio Gattone,