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Full-Text Articles in Physical Sciences and Mathematics

Higher Order Markov Structure-Based Logistic Model And Likelihood Inference For Ordinal Data, Soma Chowdhury Biswas, M. Ataharul Islam, Jamal Nazrul Islam Nov 2011

Higher Order Markov Structure-Based Logistic Model And Likelihood Inference For Ordinal Data, Soma Chowdhury Biswas, M. Ataharul Islam, Jamal Nazrul Islam

Journal of Modern Applied Statistical Methods

Azzalini (1994) proposed a first order Markov chain for binary data. Azzalini’s model is extended for ordinal data and introduces a second order model. Further, the test statistics are developed and the power of the test is determined. An application using real data is also presented.


Inference In Reversible Markov Chains, Tara Steuber Aug 2011

Inference In Reversible Markov Chains, Tara Steuber

All Dissertations

This dissertation describes the research that we have done concerning
reversible Markov chains. We first present definitions for what it means
for a Markov chain to be reversible. We then give applications of where
reversible Markov chains are used and give a brief history of Markov chain
inference. Finally, two journal articles are found in the paper, one that
is already published and another which is currently being submitted.
The first article examines estimation of the one-step-ahead
transition probabilities in a reversible Markov chain on a countable state
space. A symmetrized moment estimator is proposed that exploits the
reversible structure. …


Logistic Regression Models For Higher Order Transition Probabilities Of Markov Chain For Analyzing The Occurrences Of Daily Rainfall Data, Narayan Chanra Sinha, M. Ataharul Islam, Kazi Saleh Ahamed May 2011

Logistic Regression Models For Higher Order Transition Probabilities Of Markov Chain For Analyzing The Occurrences Of Daily Rainfall Data, Narayan Chanra Sinha, M. Ataharul Islam, Kazi Saleh Ahamed

Journal of Modern Applied Statistical Methods

Logistic regression models for transition probabilities of higher order Markov models are developed for the sequence of chain dependent repeated observations. To identify the significance of these models and their parameters a test procedure for a likelihood ratio criterion is developed. A method of model selection is suggested on the basis of AIC and BIC procedures. The proposed models and test procedures are applied to analyze the occurrences of daily rainfall data for selected stations in Bangladesh. Based on results from these models, the transition probabilities of first order Markov model for temperature and humidity provided the most suitable option …