Latent Markov Models for Longitudinal Data

Drawing on the authors' extensive research in the analysis of categorical longitudinal data, this book focuses on the formulation of latent Markov models and the practical use of these models. It demonstrates how to use the models in three types of analysis, with numerous examples illustrating how latent Markov models are used in economics, educ

Autorentext

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.

Zusammenfassung
Drawing on the authors' extensive research in the analysis of categorical longitudinal data, this book focuses on the formulation of latent Markov models and the practical use of these models. It demonstrates how to use the models in three types of analysis, with numerous examples illustrating how latent Markov models are used in economics, educ

Inhalt

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.