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      저자 : Narayan Chandra Sinha (검색결과 2 건)
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        CHAIN DEPENDENCE AND STATIONARITY TEST FOR TRANSITION PROBABILITIES OF MARKOV CHAIN UNDER LOGISTIC REGRESSION MODEL

        Sinha Narayan Chandra,Islam M. Ataharul,Ahmed Kazi Saleh The Korean Statistical Society 2006 Journal of the Korean Statistical Society Vol.35 No.4

        To identify whether the sequence of observations follows a chain dependent process and whether the chain dependent or repeated observations follow stationary process or not, alternative procedures are suggested in this paper. These test procedures are formulated on the basis of logistic regression model under the likelihood ratio test criterion and applied to the daily rainfall occurrence data of Bangladesh for selected stations. These test procedures indicate that the daily rainfall occurrences follow a chain dependent process, and the different types of transition probabilities and overall transition probabilities of Markov chain for the occurrences of rainfall follow a stationary process in the Mymensingh and Rajshahi areas, and non-stationary process in the Chittagong, Faridpur and Satkhira areas.

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        Chain Dependence and Stationarity Test for Transition Probabilities of Markov Chain under Logistic Regression Model

        Narayan Chandra Sinha,M. Ataharul Islam,Kazi Saleh Ahmed 한국통계학회 2006 Journal of the Korean Statistical Society Vol.35 No.4

        To identify whether the sequence of observations follows a chain depen-dent process and whether the chain dependent or repeated observations fol-low a stationary process or not, alternative procedures are suggested in thispaper. These test procedures are formulated on the basis of logistic re-gression model under the likelihod ratio test criterion and applied to thedaily rainfall occurrence data of Bangladesh for selected stations. Thesetest procedures indicate that the daily rainfall occurrences follow a chaindependent process, and the dierent ypes of transition probabilities andoverall transition probabilities of Markov chain for the occurrences of rain-fall follow a stationary process in the Mymensingh and Rajshahi areas, andnon-stationary process in the Chittagong, Faridpur and Satkhira areas.AMS 2000 subject classications.Primary 62M02; Secondary 62J12.Keywords. Transition probabilities, logistic regression, Markov chain, ML estimation,likelihood ratio test, daily rainfall occurrence data.1. IntroductionMarkov chain provides probability models under stochastic process to describedierent ypes of transition probabilities for chain or time dependent data. Thelogistic regression model is also a probabilistic model for analyzing binary data.By utilizing logistic regression model Muenz and Rubinstein (1985) developeddierent ypes of covariate dependent transition probabilities of Markov chain.Received May 2005; accepted December 2005.1Corresponding author. Monitoring Cell, Finance Division, Ministry of Finance, Dhaka-1000,Bangladesh (e-mail: ncsinha2002@yahoo.com)

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