Overview on Latent Markov Modeling. Background on Latent Variable and Markov Chain Models. Basic Latent Markov Model. Constrained Latent Markov Models. Including Individual Covariates and Relaxing Basic Model Assumptions. Including Random Effects and Extension to Multilevel Data. Advanced Topics about Latent Markov Modeling. Bayesian Latent Markov Models. Appendix. Bibliography. Index.
Francesco Bartolucci is a professor of statistics in the Department of Economics, Finance and Statistics at the University of Perugia, where he also coordinates the Ph.D. program in mathematical and statistical methods for the economic and social sciences. His main research interests include latent variable models for cross-sectional and longitudinal categorical data, with applications ranging from educational and psychometric contexts to the analysis of labor market data. Alessio Farcomeni is a researcher at the University of Rome "La Sapienza". His interests range from analysis of panel data and categorical time series to multiple testing, multivariate analysis and clustering, and model selection. Fulvia Pennoni is an assistant professor of statistics in the Department of Statistics at the University of Milano-Bicocca. Her main expertise encompasses latent variable modeling. She is currently carrying out research in methods and statistics with intensive statistical programming applications.
"I enjoyed reading this book very much: the writing style is clear
and concise, and the mathematical presentation is easy to follow.
Notations are well thought out and the technical derivations are
thorough. The book is a valuable resource on latent Markov models
to students, researchers, and practitioners."
—Alexander R. De Leon, Technometrics, February 2015"… a useful
contribution to the literature. … The exposition is easy to follow
for anyone who has encountered random effects models for
longitudinal data. … The overall structure is well thought out. …
The authors clearly have considerable practical experience in the
application of this technique, and they have made important
contributions to its literature."
—Geoff Jones, Australian & New Zealand Journal of Statistics, 56,
2014"The book gives an excellent introduction as well as coverage
of theoretical basics of latent Markov model analysis and their
practical applications. … I enjoyed reading the book, its clarity
of exposition, its fairly compact format, and carefully worked out
examples that did a good job in illustrating the background
theory."
—Seppo Pynnönen, International Statistical Review, 2014
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