TITLE:
Parameter Estimation in Logistic Regression for Transition, Reverse Transition and Repeated Transition from Repeated Outcomes
AUTHORS:
Rafiqul I. Chowdhury, M. Ataharul Islam, Shahariar Huda, Laurent Briollais
KEYWORDS:
Computer Program; Markov Model; Transition; Reverse Transition; Repeated Transition
JOURNAL NAME:
Applied Mathematics,
Vol.3 No.11A,
November
27,
2012
ABSTRACT: Covariate dependent Markov models dealing with estimation of transition probabilities for higher orders appear to be restricted because of over-parameterization. An improvement of the previous methods for handling runs of events by expressing the conditional probabilities in terms of the transition probabilities generated from Markovian assumptions was proposed using Chapman-Kolmogorov equations. Parameter estimation of that model needs extensive pre-processing and computations to prepare data before using available statistical softwares. A computer program developed using SAS/IML to estimate parameters of the model are demonstrated, with application to Health and Retirement Survey (HRS) data from USA.