Bayesian estimation of non-stationary Markov models combining micro and macro data
In this poster a Bayesian estimation framework for a non-stationary Markov model is developed for situations where sample data with observed transition between classes (micro data) and aggregate population shares (macro data) are available. Posterior distributions on transition probabilities are derived based on a micro based prior and a macro based Likelihood function thereby consistently combining previously separated approaches. Monte Carlo simulations for ordered and unordered Markov states show how observed micro transitions improve precision of posterior knowledge as the sample size increases.
|Date of creation:||24 Jul 2011|
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- D. L. Hawkins & Chien-Pai Han, 2000. "Estimating Transition Probabilities from Aggregate Samples Plus Partial Transition Data," Biometrics, The International Biometric Society, vol. 56(3), pages 848-854, 09.
- MacRae, Elizabeth Chase, 1977. "Estimation of Time-Varying Markov Processes with Aggregate Data," Econometrica, Econometric Society, vol. 45(1), pages 183-98, January.
- Gillian A. Lancaster & Mick Green & Steven Lane, 2006. "Reducing bias in ecological studies: an evaluation of different methodologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 681-700.
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