A First Course in Linear Model Theory
This book represents an entirely fresh approach for introducing to students the mathematical and statistical concepts and tools that form the backbone for the study of the theory and applications of linear models, both univariate and multivariate. This second edition features several new topics that are extremely relevant to the current research in statistical methodology. Revised or expanded topics include analysis of covariance, Bayesian linear and generalized linear model, nonlinear regression, ridge regression, resistant and robust regression, generalized linear models, model selection, multiple comparisons, hierarchical linear models, random effects and variance components.
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