Classification in segmented regression problems
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.
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- Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," Review of Economic Studies, Oxford University Press, vol. 74(3), pages 763-789.
- Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
- Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
- David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika van der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639.
- Cathy W. S. Chen & Mike K. P. So & Ming-Tien Chen, 2005. "A Bayesian threshold nonlinearity test for financial time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(1), pages 61-75.
- Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
- Jushan Bai, 1997. "Estimation Of A Change Point In Multiple Regression Models," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 551-563, November.
- Ram C. Tiwari & Kathleen A. Cronin & William Davis & Eric J. Feuer & Binbing Yu & Siddhartha Chib, 2005. "Bayesian model selection for join point regression with application to age-adjusted cancer rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(5), pages 919-939.
- Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 249-282, September.
- Michel Wedel & Wayne DeSarbo, 1995. "A mixture likelihood approach for generalized linear models," Journal of Classification, Springer;The Classification Society, vol. 12(1), pages 21-55, March.
- Cheon, Sooyoung & Kim, Jaehee, 2010. "Multiple change-point detection of multivariate mean vectors with the Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 406-415, February.
- Lee, Chung-Bow, 1998. "Bayesian analysis of a change-point in exponential families with applications," Computational Statistics & Data Analysis, Elsevier, vol. 27(2), pages 195-208, April.
- Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
- Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
- Chen, Cathy W.S. & So, Mike K.P., 2006. "On a threshold heteroscedastic model," International Journal of Forecasting, Elsevier, vol. 22(1), pages 73-89.
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