Paperback : HK$1,900.00
The first edition of Multivariate Statistical Modelling provided an extension of classical models for regression, time series, and longitudinal data to a much broader class including categorical data and smoothing concepts. Generalized linear modesl for univariate and multivariate analysis build the central concept, which for the modelling of complex data is widened to much more general modelling approaches. The primary aim of the new edition is to bring the book up-to-date and to reflect the major new developments over the past years. The authors give a detailed introductory survey of the subject based on the alaysis of real data drawn from a variety of subjects, including the biological sciences, economics, and the social sciences. Technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. The appendix serves as a reference or brief tutorial for the concepts of EM algorithm, numberical integration, MCMC and others. The topics covered inlude: Models for multi-categorial responses, model checking, semi- and nonparametric modelling, time series and longitudinal data, random effects models, state-space models, and survival analysis. In the new edition Bayesian concepts which are of growing importance in statistics are treated more extensively. The chapter on nonparametric and semiparametric generalized regression has been rewritten totally, random effects models now cover nonparametric maximum likelihood and fully Bayesian approaches, and state-space and hidden Markov models have been supplemented with an extension to models that can accommodate for spatial and spatiotemporal data. The authors have taken great pains to discuss theunderlying theoretical ideas in ways that relate well to the data at hand. As a result, this book is ideally suited for applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis from econometrics, biometrics and the social sciences.
Show moreThe first edition of Multivariate Statistical Modelling provided an extension of classical models for regression, time series, and longitudinal data to a much broader class including categorical data and smoothing concepts. Generalized linear modesl for univariate and multivariate analysis build the central concept, which for the modelling of complex data is widened to much more general modelling approaches. The primary aim of the new edition is to bring the book up-to-date and to reflect the major new developments over the past years. The authors give a detailed introductory survey of the subject based on the alaysis of real data drawn from a variety of subjects, including the biological sciences, economics, and the social sciences. Technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. The appendix serves as a reference or brief tutorial for the concepts of EM algorithm, numberical integration, MCMC and others. The topics covered inlude: Models for multi-categorial responses, model checking, semi- and nonparametric modelling, time series and longitudinal data, random effects models, state-space models, and survival analysis. In the new edition Bayesian concepts which are of growing importance in statistics are treated more extensively. The chapter on nonparametric and semiparametric generalized regression has been rewritten totally, random effects models now cover nonparametric maximum likelihood and fully Bayesian approaches, and state-space and hidden Markov models have been supplemented with an extension to models that can accommodate for spatial and spatiotemporal data. The authors have taken great pains to discuss theunderlying theoretical ideas in ways that relate well to the data at hand. As a result, this book is ideally suited for applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis from econometrics, biometrics and the social sciences.
Show more1. Introduction.- 2. Modelling and Analysis of Cross-Sectional Data: A Review of Univariate Generalized Linear Models.- 3. Models for Multicategorical Responses: Multivariate Extensions of Generalized Linear Models.- 4. Selecting and Checking Models.- 5. Semi- and Nonparametric Approaches to Regression Analysis.- 6. Fixed Parameter Models for Time Series and Longitudinal Data.- 7. Random Effects Models.- 8. State Space and Hidden Markov Models.- 9. Survival Models.- A..- A.1 Exponential Families and Generalized Linear Models.- A.2 Basic Ideas for Asymptotics.- A.3 EM Algorithm.- A.4 Numerical Integration.- A.5 Monte Carlo Methods.- B. Software for Fitting Generalized Linear Models and Extensions.- Author Index.
2nd edition
From the reviews of the second edition: TECHNOMETRICS "A 25% size increase in a very generous effort for a new edition of a statistics book. If you own and like the 1E, then a purchase of the 2E would certainly seem appropriate. Anyone who deals with multivariate modeling should certainly purchase a copy. This book does not have a competitor for analyzing multivariate data with generalized linear models." "The authors obviously put a great deal of work into this book … . There are nearly 40 examples … drawn from a variety of fields, extensively worked, and then reworked in succeeding chapters. … The vast amount of material is accurately presented … and laid out in an orderly and clear manner. … I conclude by endorsing this book whole-heartedly. Fahrmeir and Tutz have given the statistics community a wonderful resource for both teaching and reference." (Rick Chappell, Journal of the American Statistical Association, Vol. 98 (463), 2003) "The 6 page subject index, the author index, the bibliography (updated considerably), and the nice LaTeX layout highlight the top quality we have come to expect from these authors and this publisher. … Statisticians everywhere will want to consult ‘Multivariate Modelling’, when confronted with multivariate data. Many scientists from the fields where examples originated will do so, too, and demand the application of the new and sophisticated procedures as described in the second edition. … Recommendation: buy." (Reinhard Vonthein, Metrika, December, 2003) "This is an excellent book. Given the activity in the field, it substantially updates the material that is contained in the first edition and contains over 700 references. As well as providing references to work that is contained in the book, it makes ample suggestions for further reading of closely related topics. The result is a comprehensive book which provides an authoritative coverage of the subject area. … This bookis a valuable edition to our library and is very highly recommended." (Paul Hewson, Journal of the Royal Statistical Society, Series A: Statistics in Society, Vol. 157 (3), 2004) "This book brings together and reviews a large part of recent advances in the type of statistical modelling that are based on or related to generalized linear models. … Many real data examples from different fields illustrate the wide variety of applications of the methods. … The strength of this book is its extensive and thorough review by means of a unified notation and set of concepts of the basic ideas of the relevant literature. … The book is well written." (Jon Stene, Mathematical Reviews, Issue 2002 h) "The aim of the new edition is to reflect the major new developments over the past years. The book is clearly written, with emphasis on basic ideas. The authors illustrate concepts with numerous examples, using real data from biological sciences, economics and social sciences. … this book gives a thorough exposition of recent developments in categorical data based on GLMs." (Oleksandr Kukush, Zentralblatt MATH, Vol. 980, 2002)
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