Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 65,23
Cantidad disponible: 10 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 75,73
Cantidad disponible: 10 disponibles
Añadir al carritoCondición: New.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 70,26
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 68,07
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 67,11
Cantidad disponible: 10 disponibles
Añadir al carritoCondición: As New. Unread book in perfect condition.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 82,57
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 68,06
Cantidad disponible: 10 disponibles
Añadir al carritoCondición: New.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 81,23
Cantidad disponible: 3 disponibles
Añadir al carritoCondición: New.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 95,41
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: Brand New. 252 pages. 9.19x6.13x0.67 inches. In Stock.
Librería: moluna, Greven, Alemania
EUR 68,68
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. 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 a.
Librería: preigu, Osnabrück, Alemania
EUR 59,80
Cantidad disponible: 5 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. Latent Markov Models for Longitudinal Data | Francesco Bartolucci (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | Taylor & Francis | EAN 9781032477541 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Librería: Mispah books, Redhill, SURRE, Reino Unido
EUR 176,50
Cantidad disponible: 1 disponibles
Añadir al carritopaperback. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Idioma: Inglés
Publicado por Taylor & Francis, Chapman And Hall/CRC, 2023
ISBN 10: 1032477547 ISBN 13: 9781032477541
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 59,90
Cantidad disponible: 2 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Drawing on the authors' extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data focuses on the formulation of latent Markov models and the practical use of these models. Numerous examples illustrate how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB® routines used for the examples are available on the authors' website.The book provides you with the essential background on latent variable models, particularly the latent class model. It discusses how the Markov chain model and the latent class model represent a useful paradigm for latent Markov models. The authors illustrate the assumptions of the basic version of the latent Markov model and introduce maximum likelihood estimation through the Expectation-Maximization algorithm. They also cover constrained versions of the basic latent Markov model, describe the inclusion of the individual covariates, and address the random effects and multilevel extensions of the model. After covering advanced topics, the book concludes with a discussion on Bayesian inference as an alternative to maximum likelihood inference.As longitudinal data become increasingly relevant in many fields, researchers must rely on specific statistical and econometric models tailored to their application. A complete overview of latent Markov models, this book demonstrates how to use the models in three types of analysis: transition analysis with measurement errors, analyses that consider unobserved heterogeneity, and finding clusters of units and studying the transition between the clusters. 254 pp. Englisch.
Idioma: Inglés
Publicado por Taylor & Francis, Chapman And Hall/CRC, 2023
ISBN 10: 1032477547 ISBN 13: 9781032477541
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 68,07
Cantidad disponible: 2 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Drawing on the authors' extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data focuses on the formulation of latent Markov models and the practical use of these models. Numerous examples illustrate how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB® routines used for the examples are available on the authors' website.The book provides you with the essential background on latent variable models, particularly the latent class model. It discusses how the Markov chain model and the latent class model represent a useful paradigm for latent Markov models. The authors illustrate the assumptions of the basic version of the latent Markov model and introduce maximum likelihood estimation through the Expectation-Maximization algorithm. They also cover constrained versions of the basic latent Markov model, describe the inclusion of the individual covariates, and address the random effects and multilevel extensions of the model. After covering advanced topics, the book concludes with a discussion on Bayesian inference as an alternative to maximum likelihood inference.As longitudinal data become increasingly relevant in many fields, researchers must rely on specific statistical and econometric models tailored to their application. A complete overview of latent Markov models, this book demonstrates how to use the models in three types of analysis: transition analysis with measurement errors, analyses that consider unobserved heterogeneity, and finding clusters of units and studying the transition between the clusters.