Classification in segmented regression problems
Cathy W. S. Chen (),
Mike K.P. So and
Kevin K.M. Lee
Computational Statistics & Data Analysis, 2011, vol. 55, issue 7, 2276-2287
Heterogeneity in many datasets stems from the different behaviors of several underlying groups or subpopulations. The aim of this paper is to classify observations in such a dataset into these latent groups when each group's behavior is piecewise linearly related to a set of covariates. We assume that each group can be represented by a segmented regression model, but the group membership for each observation is unobserved or lost. A full Bayesian approach is proposed to simultaneously classify observations and estimate segmented regression parameters. The estimated marginal likelihood and the Deviance Information Criterion are used to select the number of mixture groups. We demonstrate the accuracy and performance of the proposed MCMC estimators in a simulation study and illustrate the methodology in an empirical study.
Keywords: Change; point; Data; augmentation; Deviance; information; criterion; Mixture; model; MCMC; Segmented; regression (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:55:y:2011:i:7:p:2276-2287
Access Statistics for this article
Computational Statistics & Data Analysis is currently edited by S.P. Azen
More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Nithya Sathishkumar ().