Often the scientist is faced with a large number of categorical variates which are of potential use in discriminating between two pregiven groups of objects. For example, an investor may wish to assign a particular firm to one of two possible risk groups based upon certain known characteristics of the firm (liquid to fixed asset ratio, etc.), or an engineer might wish to determine which of two models best describes a particular situation based upon the observed characteristics of situation. This is the general problem of variable selection in discriminant analysis. When obtaining and processing the numerous variables is expensive, one must select a best subset of variables which incorporates as much information for discriminating as possible. If time is also a factor, a stepwise procedure is mandated. We propose such a stepwise procedure here based upon information theoretic considerations. (Author).

This introduction to the MDL Principle provides a reference accessible to graduate students and researchers in statistics, pattern classification, machine learning, and data mining, to philosophers interested in the foundations of statistics, and to researchers in other applied sciences that involve model selection.

Statistical Methods of Discrimination and Classification: Advances in Theory and Applications is a collection of papers that tackles the multivariate problems of discriminating and classifying subjects into exclusive population. The book presents 13 papers that cover that advancement in the statistical procedure of discriminating and classifying. The studies in the text primarily focus on various methods of discriminating and classifying variables, such as multiple discriminant analysis in the presence of mixed continuous and categorical data; choice of the smoothing parameter and efficiency of k-nearest neighbor classification; and assessing the performance of an allocation rule. The book will be of great use to researchers and practitioners of wide array of scientific disciplines, including engineering, psychology, biology, and physics.

From the reviews of the First Edition. "An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references." —Choice "Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent." —Contemporary Sociology "An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical." —The Statistician In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.