Model-based two-way clustering of second-level units in ordinal multilevel latent Markov models

Giorgio Eduardo Montanari (), Marco Doretti and Maria Francesca Marino
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Giorgio Eduardo Montanari: University of Perugia
Marco Doretti: University of Perugia
Maria Francesca Marino: University of Florence

Advances in Data Analysis and Classification, 2022, vol. 16, issue 2, No 10, 457-485

Abstract: Abstract In this paper, an ordinal multilevel latent Markov model based on separate random effects is proposed. In detail, two distinct second-level discrete effects are considered in the model, one affecting the initial probability vector and the other affecting the transition probability matrix of the first-level ordinal latent Markov process. To model these separate effects, we consider a bi-dimensional mixture specification that allows to avoid unverifiable assumptions on the random effect distribution and to derive a two-way clustering of second-level units. Starting from a general model where the two random effects are dependent, we also obtain the independence model as a special case. The proposal is applied to data on the physical health status of a sample of elderly residents grouped into nursing homes. A simulation study assessing the performance of the proposal is also included.

Keywords: Latent Markov model; Multilevel modeling; Nursing home; Random effect separation; 62H30 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11634-021-00446-7

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