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Statistical Methods for Categorical Data Analysis Daniel A. Powers
Publisher: Academic Press

You want to know about a technique from the pre-computer days, with examples? In the present work, we propose an Aggregated-Multifactor Dimensionality Reduction (A-MDR) method that exhaustively searches for statistically significant gene-gene (GxG) interactions to generate a gene interaction network. Includes some very useful expositions on how to analyze categorical data with regression techniques. We haven't caught up to it yet. Presenting a broad scope of coverage and the latest research on the topic, the author begins with categorical data analysis methods for cross-sectional, prospective, and retrospective studies of binary, polychotomous, and ordinal data. Categorical Data Analysis In this'chapter'we consider: (1) a test for fit of a distribution, focusing on completely specified distributions, with some broader discussion. Multiple Regression in Behavioral Research. Although one can employ logistic regression or other standard multivariate categorical data analysis approaches to explore interactions among SNPs, there are an enormous number of possible interactions in a model with both linear and nonlinear effects. These include models involving random effects, covariance analysis, variable subset selection in regression methods, categorical data analysis, and graphical tools for residual diagnostics. Reference for biostatisticians and epidemiologists." —International Statistical Institute A new edition of the definitive guide to classical and modern methods of biostatistics.