22 citations · 63 across the 13 of their papers we have counts for
5 papers · 1 filter
A discrete time event-history approach to informative drop-out in multivariate latent Markov models with covariates
Francesco Bartolucci, Alessio Farcomeni
Latent Markov (LM) models represent an important tool of analysis of longitudinal data when response variables are affected by time-varying unobserved heterogeneity, which is accou…
Causal inference in paired two-arm experimental studies under non-compliance with application to prognosis of myocardial infarction
F. Bartolucci, A. Farcomeni
Motivated by a study about prompt coronary angiography in myocardial infarction, we propose a method to estimate the causal effect of a treatment in two-arm experimental studies wi…
Bayesian inference through encompassing priors and importance sampling for a class of marginal models for categorical data
Francesco Bartolucci, Luisa Scaccia, Alessio Farcomeni
We develop a Bayesian approach for selecting the model which is the most supported by the data within a class of marginal models for categorical variables formulated through equali…
A note on the application of the Oakes' identity to obtain the observed information matrix of hidden Markov models
F. Bartolucci, A. Farcomeni, F. Pennoni
We derive the observed information matrix of hidden Markov models by the application of the Oakes (1999)'s identity. The method only requires the first derivative of the forward-ba…
An overview of latent Markov models for longitudinal categorical data
F. Bartolucci, A. Farcomeni, F. Pennoni
We provide a comprehensive overview of latent Markov (LM) models for the analysis of longitudinal categorical data. The main assumption behind these models is that the response var…