At last, after a decade of mounting interest in log-linear and related models for the analysis of discrete multivariate data, particularly in the form of multidimensional tables, we now have a comprehensive text and general reference on the subject. Even a mediocre attempt to organize the extensive and widely scattered literature on discrete multivariate analysis would be welcome; happily, this is an excellent such effort, but a group of Harvard statisticians that has contributed much to the field. Their book ought to serve as a basic guide to the analysis of quantitative data for years to come. - James R. Beninger, Contemporary Sociology
"A welcome addition to multivariate analysis. The discussion is lucid and very leisurely, excellently illustrated with applications drawn from a wide variety of fields. A good part of the book can be understood without very specialized statistical knowledge. It is a most welcome contribution to an interesting and lively subject." - D.R. Cox, Nature
"Discrete Multivariate Analysis is an ambitious attempt to present log-linear models to a broad audience. Exposition is quite discursive, and the mathematical level, except in Chapters 12 and 14, is very elementary. To illustrate possible applications, some 60 different sets of data have been gathered together from diverse fields. To aid the reader, an index of these examples has been provided. ...the book contains a wealth of material on important topics. Its numerous examples are especially valuable." - Shelby J. Haberman, The Annals of Statistics
From the reviews: "The book deals with discrete multivariate analysis in an effort to bring together in an organised way the extensive theory and practice existing in this field. It is organised in 14 chapters. ! is well addressed to readers from different background and different interests covering a wide range from graduate students in theoretical statistics to quantitative biological or social scientists, applied statisticians and other quantitative research workers looking for comprehensive analyses of discrete multivariate data." (Christina Diakaki, Zentralblatt MATH, Vol. 1131 (9), 2008)
Introduction.- Structural models for counted data.- Maximum likelihood estimates for complete tables.- Formal goodness of fit: Summary statistics and model selection.- Maximum likelihood estimation for incomplete tables.- Estimating the size of a closed population.- Models for measuring change.- Analysis of square tables: Symmetry and marginal homogeneity.- Model selection and assessing closeness of fit: Practical aspects.- Other methods for estimation and testing in cross-classifications.- Measures of association and agreement.- Pseudo-Bayes estimates of cell probabilites.- Sampling models for discrete data.- Asymptotic methods.
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