Homogeneity versus Parsimony in Markov Manpower Models: A Hidden Markov Chain Approach
Everestus O. Ossai *
Department of Statistics, University of Nigeria, Nsukka, Nigeria.
Precious N. Ezra
Department of Statistics, University of Nigeria, Nsukka, Nigeria.
Felix O. Ohanuba
Department of Statistics, University of Nigeria, Nsukka, Nigeria.
Martin N. Eze
Department of Statistics, University of Nigeria, Nsukka, Nigeria.
*Author to whom correspondence should be addressed.
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.
Keywords: Statistical manpower planning, hidden Markov model, homogeneity, parsimony