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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
mathematics
Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models
Statistical Methods in Medical Research, Volume 28, No. 5, Year 2019
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Description
Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as ‘Low’, ‘Moderate’ and ‘High’ with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components. © The Author(s) 2018.
Authors & Co-Authors
Sebastian, Tunny
India, Vellore
Christian Medical College, Vellore
Jeyaseelan, Visalakshi
India, Vellore
Christian Medical College, Vellore
Jeyaseelan, Lakshamanan
India, Vellore
Christian Medical College, Vellore
George, Sebastian
India, Kottayam
St. Thomas College Palai
Bangdiwala, Shrikant I.
Canada, Hamilton
Mcmaster University
Statistics
Citations: 9
Authors: 5
Affiliations: 3
Identifiers
Doi:
10.1177/0962280218766964
ISSN:
09622802
Study Approach
Quantitative