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M. A. Islam and R. I. Chowdhury, “A Higher-Order Markov Model for Analyzing Covariate Dependence,” Applied Mathematical Modelling, Vol. 30, No. 6, 2006, pp. 477-488. doi:10.1016/j.apm.2005.05.006

has been cited by the following article:

• 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.