Autori
Carette, Philippe
Guerry, Marie-Anne

Titolo
Markov models for duration-dependent transitions: selecting the states using duration values or duration intervals?
Periodico
Statistical methods & applications : Journal of the Italian Statistical Society
Anno: 2022 - Volume: 31 - Fascicolo: 5 - Pagina iniziale: 1203 - Pagina finale: 1223

In a Markov model the transition probabilities between states do not depend on the time spent in the current state. The present paper explores two ways of selecting the states of a discrete-time Markov model for a system partitioned into categories where the duration of stay in a category affects the probability of transition to another category. For a set of panel data, we compare the likelihood fits of the Markov models with states based on duration intervals and with states defined by duration values. For hierarchical systems, we show that the model with states based on duration values has a better maximum likelihood fit than the baseline Markov model where the states are the categories. We also prove that this is not the case for the duration-interval model, under conditions on the data that seem realistic in practice. Furthermore, we use the Akaike and Bayesian information criteria to compare these alternative Markov models. The theoretical findings are illustrated by an analysis of a real-world personnel data set.



SICI: 1618-2510(2022)31:5<1203:MMFDTS>2.0.ZU;2-N

Esportazione dati in Refworks (solo per utenti abilitati)

Biblioteche ACNP che possiedono il periodico