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Research Article Open access CC BY 4.0

Homogeneity versus Parsimony in Markov Manpower Models: A Hidden Markov Chain Approach

Everestus O. Ossai, Precious N. Ezra, Felix O. Ohanuba, Martin N. Eze

Asian Journal of Probability and Statistics · pp. 82–93 · Published 5 Dec 2022

10.9734/ajpas/2022/v20i4441

Abstract

We aim at tackling the problem of inadequate specification of a Markov manpower model in this paper, by formulating a procedure for validating the inclusion or non-inclusion of some transition parameters in the model. The mover-stayer principle and its extensions are employed to incorporate hidden classes in the model to achieve more homogeneity and this is compared with the model without the hidden classes, which is more parsimonious, using Likelihood ratio statistic, Akaike Information Criterion and Bayesian Information Criterion. The illustration shows a case of manpower data where, up to a certain level of hidden states, homogeneity is more important than parsimony.

Statistical manpower planning hidden Markov model homogeneity parsimony

Cited by 2

Likelihood Ratio Search Procedure for Optimum Number of States in a Hidden Markov Manpower Model

Everestus O. Ossai, Mbanefo S. Madukaife · Asian Journal of Probability and Statistics · 2022

An extended Markov-switching model approach to latent heterogeneity in departmentalized manpower systems

Everestus O. Ossai, Uchenna C. Nduka, Mbanefo S. Madukaife · Communications in Statistics - Theory and Methods · 2023

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