Abstract
This chapter deals with likelihood-based inference for ergodic finite as well as infinite Markov chains. We also consider extensions of Markov chain models, such as Hidden Markov chain, Markov chains based on polytomous regression, and Raftery’s Mixture Transition Density model. These models have less number of parameters as compared to a higher order finite Markov chain. Lastly, we discuss methods of estimation in grouped data from finite Markov chains.
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Rajarshi, M.B. (2013). Markov Chains and Their Extensions. In: Statistical Inference for Discrete Time Stochastic Processes. SpringerBriefs in Statistics. Springer, India. https://doi.org/10.1007/978-81-322-0763-4_2
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DOI: https://doi.org/10.1007/978-81-322-0763-4_2
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