11 citations · 16 across the 7 of their papers we have counts for
7 papers
Maximum likelihood estimation of hidden Markov models for continuous longitudinal data with missing responses and dropout
Silvia Pandolfi, Francesco Bartolucci, Fulvia Pennoni
We propose an inferential approach for maximum likelihood estimation of the hidden Markov models for continuous responses. We extend to the case of longitudinal observations the fi…
LMest: an R package for latent Markov models for categorical longitudinal data
Francesco Bartolucci, Alessio Farcomeni, Silvia Pandolfi +1
Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data (Bartolucci et. al., 2013), especially when response variables are categorica…
Item selection by Latent Class-based methods
Francesco Bartolucci, Giorgio E. Montanari, Silvia Pandolfi
The evaluation of nursing homes is usually based on the administration of questionnaires made of a large number of polytomous items. In such a context, the Latent Class (LC) model…
Three-step estimation of latent Markov models with covariates
Francesco Bartolucci, Giorgio E. Montanari, Silvia Pandolfi
We propose a modified version of the three-step estimation method for the latent class model with covariates, which may be used to estimate latent Markov models for longitudinal da…
A comparison of some criteria for states selection in the latent Markov model for longitudinal data
Silvia Bacci, Silvia Pandolfi, Fulvia Pennoni
We compare different selection criteria to choose the number of latent states of a multivariate latent Markov model for longitudinal data. This model is based on an underlying Mark…
Bayesian inference for a class of latent Markov models for categorical longitudinal data
Francesco Bartolucci, Silvia Pandolfi
We propose a Bayesian inference approach for a class of latent Markov models. These models are widely used for the analysis of longitudinal categorical data, when the interest is i…
A generalized Multiple-try Metropolis version of the Reversible Jump algorithm
S. Pandolfi, F. Bartolucci, N. Friel
The Reversible Jump algorithm is one of the most widely used Markov chain Monte Carlo algorithms for Bayesian estimation and model selection. A generalized multiple-try version of…