Автор: Jiang Jiming, Nguyen Thuan Название: Linear and Generalized Linear Mixed Models and Their Applications ISBN: 1071612816 ISBN-13(EAN): 9781071612811 Издательство: Springer Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. Furthermore, it includes recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models.
Автор: Jiang Jiming, Nguyen Thuan Название: Linear and Generalized Linear Mixed Models and Their Applications ISBN: 1071612840 ISBN-13(EAN): 9781071612842 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. Furthermore, it includes recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models.
Автор: Berridge, Damon Mark Название: Multivariate Generalized Linear Mixed Models Using R ISBN: 1439813264 ISBN-13(EAN): 9781439813263 Издательство: Taylor&Francis Рейтинг: Цена: 102080.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Faraway, Julian J. (university Of Bath, United Kingdom) Название: Extending the linear model with r ISBN: 149872096X ISBN-13(EAN): 9781498720960 Издательство: Taylor&Francis Рейтинг: Цена: 93910.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Salinas Ru?z Название: Generalized Linear Mixed Models with Applications in Agriculture and Biology ISBN: 3031327993 ISBN-13(EAN): 9783031327995 Издательство: Springer Рейтинг: Цена: 37260.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached from an applications perspective, without neglecting the rigor of the theory. For this reason, the theory that supports each of the studied methods is addressed and later - through examples - its application is illustrated. In addition, some of the assumptions and shortcomings of linear statistical models in general are also discussed. An alternative to analyse non-normal distributed response variables is the use of generalized linear models (GLM) to describe the response data with an exponential family distribution that perfectly fits the real response. Extending this idea to models with random effects allows the use of Generalized Linear Mixed Models (GLMMs). The use of these complex models was not computationally feasible until the recent past, when computational advances and improvements to statistical analysis programs allowed users to easily, quickly, and accurately apply GLMM to data sets. GLMMs have attracted considerable attention in recent years. The word "Generalized" refers to non-normal distributions for the response variable and the word "Mixed" refers to random effects, in addition to the fixed effects typical of analysis of variance (or regression). With the development of modern statistical packages such as Statistical Analysis System (SAS), R, ASReml, among others, a wide variety of statistical analyzes are available to a wider audience. However, to be able to handle and master more sophisticated models requires proper training and great responsibility on the part of the practitioner to understand how these advanced tools work. GMLM is an analysis methodology used in agriculture and biology that can accommodate complex correlation structures and types of response variables.
Автор: Salinas Ru?z Название: Generalized Linear Mixed Models with Applications in Agriculture and Biology ISBN: 3031328027 ISBN-13(EAN): 9783031328022 Издательство: Springer Рейтинг: Цена: 37260.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached from an applications perspective, without neglecting the rigor of the theory. For this reason, the theory that supports each of the studied methods is addressed and later - through examples - its application is illustrated. In addition, some of the assumptions and shortcomings of linear statistical models in general are also discussed. An alternative to analyse non-normal distributed response variables is the use of generalized linear models (GLM) to describe the response data with an exponential family distribution that perfectly fits the real response. Extending this idea to models with random effects allows the use of Generalized Linear Mixed Models (GLMMs). The use of these complex models was not computationally feasible until the recent past, when computational advances and improvements to statistical analysis programs allowed users to easily, quickly, and accurately apply GLMM to data sets. GLMMs have attracted considerable attention in recent years. The word "Generalized" refers to non-normal distributions for the response variable and the word "Mixed" refers to random effects, in addition to the fixed effects typical of analysis of variance (or regression). With the development of modern statistical packages such as Statistical Analysis System (SAS), R, ASReml, among others, a wide variety of statistical analyzes are available to a wider audience. However, to be able to handle and master more sophisticated models requires proper training and great responsibility on the part of the practitioner to understand how these advanced tools work. GMLM is an analysis methodology used in agriculture and biology that can accommodate complex correlation structures and types of response variables.
Автор: Legler Название: Generalized Linear Models & Correla ISBN: 1439885389 ISBN-13(EAN): 9781439885383 Издательство: Taylor&Francis Рейтинг: Цена: 91860.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: For advanced undergraduate or non-major graduate students in Advanced Statistical Modeling or Regression II and courses in Generalized Linear Models, Longitudinal Data Analysis, Correlated Data, Multilevel Models. Material on R at the end of each chapter. Solutions manual for qualified instructors.
Автор: Robert Gilchrist; Brian Francis; Joe Whittaker Название: Generalized Linear Models ISBN: 0387962247 ISBN-13(EAN): 9780387962245 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: W. Hennevogl; Ludwig Fahrmeir; Gerhard Tutz Название: Multivariate Statistical Modelling Based on Generalized Linear Models ISBN: 1441929002 ISBN-13(EAN): 9781441929006 Издательство: Springer Рейтинг: Цена: 181670.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis. This second edition is extensively revised, especially those sections relating with Bayesian concepts.
Автор: Dobson, Annette J. (university Of Queensland, Herston, Australia) Barnett, Adrian (queensland University Of Technology, Kelvin Grove, Australia) Название: Introduction to generalized linear models, fourth edition ISBN: 1138741515 ISBN-13(EAN): 9781138741515 Издательство: Taylor&Francis Рейтинг: Цена: 38720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice.
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