Poisson-hidden Markov Model for Overdispersed Time Series Count Data

Authors

  • Joshni George
  • Seemon Thomas

DOI:

https://doi.org/10.37591/rrjost.v6i3.129

Abstract

The fitting of Poisson hidden Markov model for overdispersed time series data is considered. The parameters of the model are estimated using numerical maximization of the likelihood and the sequence of hidden states are obtained based on the best fitted model. Pseudoresiduals obtained by the fitted model are also used along with AIC and BIC values to check the goodness of fit of the model.

Keywords: Hidden Markov model, transition probability, stationary distribution, Viterbi algorithm

Cite this Article Joshni George, Seemon Thomas. Poissonhidden Markov Model for Overdispersed Time Series Count Data. Research & Reviews: Journal of Statistics. 2017; 6(3): 56–61p. 

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Published

2018-01-04

Issue

Vol. 6 No. 3 (2017)

Section

Research Article

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