{"product_id":"9780470073711","title":"Generalized, Linear, and Mixed Models","description":"\u003ch3\u003eWiley Series in Probability and Statistics\u003c\/h3\u003e\u003ch1\u003eGeneralized, Linear, and Mixed Models\u003c\/h1\u003e\u003ch3\u003eCharles E. McCulloch | Shayle R. Searle | John M. Neuhaus\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eSocial Science \/ Statistics\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cb\u003eAn accessible and self-contained introduction to statistical models-now in a modernized new edition\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e \u003ci\u003eGeneralized, Linear, and Mixed Models, Second Edition\u003c\/i\u003e provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects.\u003cbr\u003e \u003cbr\u003e A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed models is maintained throughout, and each chapter illustrates how these models are applicable in a wide array of contexts. In addition, a discussion of general methods for the analysis of such models is presented with an emphasis on the method of maximum likelihood for the estimation of parameters. The authors also provide comprehensive coverage of the latest statistical models for correlated, non-normally distributed data. Thoroughly updated to reflect the latest developments in the field, the \u003ci\u003eSecond Edition\u003c\/i\u003e features:  \u003cul\u003e \u003cli\u003eA new chapter that covers omitted covariates, incorrect random effects distribution, correlation of covariates and random effects, and robust variance estimation\u003c\/li\u003e \u003cli\u003eA new chapter that treats shared random effects models, latent class models, and properties of models\u003c\/li\u003e \u003cli\u003eA revised chapter on longitudinal data, which now includes a discussion of generalized linear models, modern advances in longitudinal data analysis, and the use between and within covariate decompositions\u003c\/li\u003e \u003cli\u003eExpanded coverage of marginal versus conditional models\u003c\/li\u003e \u003cli\u003eNumerous new and updated examples\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e With its accessible style and wealth of illustrative exercises, \u003ci\u003eGeneralized, Linear, and Mixed Models\u003c\/i\u003e, Second Edition is an ideal book for courses on generalized linear and mixed models at the upper-undergraduate and beginning-graduate levels. It also serves as a valuable reference for applied statisticians, industrial practitioners, and researchers.\u003c\/div\u003e\u003cdiv\u003e  \u003cb\u003eCharles E. McCulloch\u003c\/b\u003e, PhD, is Professor and Head of the Division of Biostatistics in the School of Medicine at the University of California, San Francisco. A Fellow of the American Statistical Association, Dr. McCulloch is the author of numerous published articles in the areas of longitudinal data analysis, generalized linear mixed models, and latent class models and their applications.  \u003cp\u003e\u003cb\u003eShayle R. Searle\u003c\/b\u003e, PhD, is Professor Emeritus in the Department of Biological Statistics and Computational Biology at Cornell University. Dr. Searle is the author of \u003ci\u003eLinear Models\u003c\/i\u003e, \u003ci\u003eLinear Models for Unbalanced Data\u003c\/i\u003e, \u003ci\u003eMatrix Algebra Useful for Statistics\u003c\/i\u003e, and \u003ci\u003eVariance Components\u003c\/i\u003e, all published by Wiley.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJohn M. Neuhaus\u003c\/b\u003e, PhD, is Professor of Biostatistics in the School of Medicine at the University of California, San Francisco. A Fellow of the American Statistical Association and the Royal Statistical Society, Dr. Neuhaus has authored or coauthored numerous journal articles on statistical methods for analyzing correlated response data and assessments on the effects of statistical model misspecification.\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e30 June 2008\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley-Interscience\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9780470073711\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e432\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e26.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44380387704972,"sku":"9780470073711","price":190.76,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9780470073711.jpg?v=1780114642","url":"https:\/\/lateknightbooks.com\/products\/9780470073711","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}