Estimating the transition matrix of a Markov chain observed at random times
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DOI: 10.1016/j.spl.2014.07.009
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- Bruce A. Craig & Peter P. Sendi, 2002. "Estimation of the transition matrix of a discrete‐time Markov chain," Health Economics, John Wiley & Sons, Ltd., vol. 11(1), pages 33-42, January.
- Robert B. Israel & Jeffrey S. Rosenthal & Jason Z. Wei, 2001. "Finding Generators for Markov Chains via Empirical Transition Matrices, with Applications to Credit Ratings," Mathematical Finance, Wiley Blackwell, vol. 11(2), pages 245-265, April.
- MacRae, Elizabeth Chase, 1977. "Estimation of Time-Varying Markov Processes with Aggregate Data," Econometrica, Econometric Society, vol. 45(1), pages 183-198, January.
- Pittenger, A. O., 1982. "Time changes of Markov chains," Stochastic Processes and their Applications, Elsevier, vol. 13(2), pages 189-199, August.
- Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
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Keywords
Time varying Markov process; Identifiability; Sparse transition matrix; Parametric estimation; Asymptotic normality; Lie bracket;All these keywords.
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