"Discrete Multivariate Analysis: Theory and Practice" by Yvonne M. M. Bishop, Stephen E. Fienberg, and Paul W. Holland, with collaboration from Richard J. Light and Frederick Mosteller, is a comprehensive textbook on the analysis of discrete multivariate data. Published by Springer, it is a reprint of the 1975 edition originally published by MIT Press. The book provides a detailed treatment of statistical methods for analyzing cross-classified data, including structural models, maximum likelihood estimation, goodness of fit, and model selection. It also covers various estimation and testing methods in cross-classifications, as well as models for measuring change and analyzing square tables. The authors emphasize the importance of understanding the structure of data and the use of appropriate statistical models for analysis. The book is aimed at statisticians, researchers, and students in the fields of statistics, biology, social sciences, and medicine. It includes a detailed reference list, appendices, and a comprehensive index. The authors also acknowledge the contributions of many colleagues and students who provided feedback and suggestions during the development of the manuscript. The book was supported by various research grants from the National Science Foundation and other organizations. The content is organized into 14 chapters, covering a wide range of topics in discrete multivariate analysis, including structural models, maximum likelihood estimation, goodness of fit, and model selection. The book is written for advanced students and researchers in statistics and related fields."Discrete Multivariate Analysis: Theory and Practice" by Yvonne M. M. Bishop, Stephen E. Fienberg, and Paul W. Holland, with collaboration from Richard J. Light and Frederick Mosteller, is a comprehensive textbook on the analysis of discrete multivariate data. Published by Springer, it is a reprint of the 1975 edition originally published by MIT Press. The book provides a detailed treatment of statistical methods for analyzing cross-classified data, including structural models, maximum likelihood estimation, goodness of fit, and model selection. It also covers various estimation and testing methods in cross-classifications, as well as models for measuring change and analyzing square tables. The authors emphasize the importance of understanding the structure of data and the use of appropriate statistical models for analysis. The book is aimed at statisticians, researchers, and students in the fields of statistics, biology, social sciences, and medicine. It includes a detailed reference list, appendices, and a comprehensive index. The authors also acknowledge the contributions of many colleagues and students who provided feedback and suggestions during the development of the manuscript. The book was supported by various research grants from the National Science Foundation and other organizations. The content is organized into 14 chapters, covering a wide range of topics in discrete multivariate analysis, including structural models, maximum likelihood estimation, goodness of fit, and model selection. The book is written for advanced students and researchers in statistics and related fields.