{"product_id":"mixed-models-theory-and-applications-with-r-hardcover","title":"Mixed Models: Theory and Applications with R - Hardcover","description":"\u003cp\u003eby \u003cb\u003eEugene Demidenko\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003ePraise for the \u003ci\u003eFirst Edition\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one's personal library.\"\u003c\/p\u003e \u003cp\u003e--\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e Mixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. \u003ci\u003eMixed Models: Theory and Applications with R, Second Edition\u003c\/i\u003e fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R.\u003c\/p\u003e \u003cp\u003eThe new edition provides in-depth mathematical coverage of mixed models' statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMixed Models: Theory and Applications with R, Second Edition\u003c\/i\u003e features unique applications of mixed model methodology, as well as: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eComprehensive theoretical discussions illustrated by examples and figures\u003c\/li\u003e \u003cli\u003eOver 300 exercises, end-of-section problems, updated data sets, and R subroutines\u003c\/li\u003e \u003cli\u003eProblems and extended projects requiring simulations in R intended to reinforce material\u003c\/li\u003e \u003cli\u003eSummaries of major results and general points of discussion at the end of each chapter\u003c\/li\u003e \u003cli\u003eOpen problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIdeal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.\u003c\/p\u003e\u003ch3\u003eFront Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003ePraise for the \u003ci\u003eFirst Edition\u003c\/i\u003e\u003cbr\u003e \u003cb\u003eThis book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one's personal library.\u003cbr\u003e --\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. \u003ci\u003eMixed Models: Theory and Applications with R, Second Edition\u003c\/i\u003e fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R.\u003c\/p\u003e \u003cp\u003eThe new edition provides in-depth mathematical coverage of mixed models' statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMixed Models: Theory and Applications with R, Second Edition\u003c\/i\u003e features unique applications of mixed model methodology, as well as: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eComprehensive theoretical discussions illustrated by examples and figures\u003c\/li\u003e \u003cli\u003eOver 300 exercises, end-of-section problems, updated data sets, and R subroutines\u003c\/li\u003e \u003cli\u003eProblems and extended projects requiring simulations in R intended to reinforce material\u003c\/li\u003e \u003cli\u003eSummaries of major results and general points of discussion at the end of each chapter\u003c\/li\u003e \u003cli\u003eOpen problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIdeal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003ePraise for the \u003ci\u003eFirst Edition\u003c\/i\u003e\u003cbr\u003e \u003cb\u003e\"This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one's personal library.\"\u003cbr\u003e --\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. \u003ci\u003eMixed Models: Theory and Applications with R, Second Edition\u003c\/i\u003e fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R.\u003c\/p\u003e \u003cp\u003eThe new edition provides in-depth mathematical coverage of mixed models' statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMixed Models: Theory and Applications with R, Second Edition\u003c\/i\u003e features unique applications of mixed model methodology, as well as: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eComprehensive theoretical discussions illustrated by examples and figures\u003c\/li\u003e \u003cli\u003eOver 300 exercises, end-of-section problems, updated data sets, and R subroutines\u003c\/li\u003e \u003cli\u003eProblems and extended projects requiring simulations in R intended to reinforce material\u003c\/li\u003e \u003cli\u003eSummaries of major results and general points of discussion at the end of each chapter\u003c\/li\u003e \u003cli\u003eOpen problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIdeal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEUGENE DEMIDENKO, PhD, \u003c\/b\u003e is Professor of Biostatistics and Epidemiology at the Geisel School of Medicine and Department of Mathematics at Dartmouth College. Dr. Demidenko carries out collaborative work at the Thayer School of Engineering, Dartmouth College, including nanocancer therapy and electrical impedance tomography for breast cancer detection. Dr. Demidenko is recipient of several awards from the American Statistical Association and has been an invited lecturer at several institutes and academies around the world.\u003c\/p\u003e\n        \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 768\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.5 x 10 x 7.1 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e August 05, 2013\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42744922374207,"sku":"9781118091579","price":330.37,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/be66e3d6cf2df75ebff30b9f532083ef.webp?v=1765170221","url":"https:\/\/dhlswag.com\/products\/mixed-models-theory-and-applications-with-r-hardcover","provider":"BBB","version":"1.0","type":"link"}