Non-Fiction Books:

Latent Markov Models for Longitudinal Data

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Description

Exploring the theory and applications of latent Markov modeling in a common conceptual framework, this book presents a nontechnical overview of latent Markov models and their potential in socio-economic applications. The statistical approach of the text emphasizes inference and the use of models in applications. The book first describes the latent Markov model proposed by Wiggins, taking into account other models in the field, such as latent transition analysis and hidden Markov analysis. The authors then lead readers to the possibility of implementing, using, and calibrating the latest developments. They also provide ad hoc MATLAB(R) routines for fitting the proposed models.

Author Biography:

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.
Release date Australia
October 29th, 2012
Audiences
  • General (US: Trade)
  • Tertiary Education (US: College)
Illustrations
68 Tables, black and white; 9 Illustrations, black and white
Pages
252
Dimensions
156x235x23
ISBN-13
9781439817087
Product ID
10354036

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