Generalized Linear Mixed Models: Modern Concepts, Methods, and Applications (2nd edition) presents an updated introduction to linear modeling using the generalized linear mixed model (GLMM) as the overarching conceptual framework. For students new to statistical modeling, this book helps them see the big picture – linear modeling as broadly understood and its intimate connection with statistical design and mathematical statistics. For readers experienced in statistical practice, but new to GLMMs, the book provides a comprehensive introduction to GLMM methodology and its underlying theory. Unlike textbooks that focus on classical linear models or generalized linear models or mixed models, this book covers all of the above as members of a unified GLMM family of linear models. In addition to essential theory and methodology, this book features a rich collection of examples using SAS® software to illustrate GLMM practice. This second edition is updated to reflect lessons learned and experience gained regarding best practices and modeling choices faced by GLMM practitioners. New to this edition are two chapters focusing on Bayesian methods for GLMMs. Key Features: • Most statistical modeling books cover classical linear models or advanced generalized and mixed models; this book covers all members of the GLMM family – classical and advanced models. • Incorporates lessons learned from experience and on-going research to provide up-to-date examples of best practices. • Illustrates connections between statistical design and modeling: guidelines for translating study design into appropriate model and in-depth illustrations of how to implement these guidelines; use of GLMM methods to improve planning and design. • Discusses the difference between marginal and conditional models, differences in the inference space they are intended to address and when each type of model is appropriate. • In addition to likelihood-based frequentist estimation and inference, provides a brief introduction to Bayesian methods for GLMMs. Walt Stroup is an Emeritus Professor of Statistics. He served on the University of Nebraska statistics faculty for over 40 years, specializing in statistical modeling and statistical design. He is a Fellow of the American Statistical Association, winner of the University of Nebraska Outstanding Teaching and Innovative Curriculum Award and author or co-author of three books on mixed models and their extensions. Marina Ptukhina (Pa-too-he-nuh), PhD, is an Associate Professor of Statistics at Whitman College. She is interested in statistical modeling, design and analysis of research studies and their applications. Her research includes applications of statistics to economics, biostatistics and statistical education. Ptukhina earned a PhD in Statistics from the University of Nebraska-Lincoln, a Master of Science degree in Mathematics from Texas Tech University and a Specialist degree in Management from The National Technical University "Kharkiv Polytechnic Institute." Julie Garai, PhD, is a Data Scientist at Loop. She earned her PhD in Statistics from the University of Nebraska-Lincoln and a bachelor’s degree in Mathematics and Spanish from Doane College. Dr Garai actively collaborates with statisticians, psychologists, ecologists, forest scientists, software engineers, and business leaders in academia and industry. In her spare time, she enjoys leisurely walks with her dogs, dance parties with her children, and playing the trombone.
The Continuum Contemporaries series gives readers accessible and informative introductions to some of the most popular, most acclaimed and most influential novels of recent years. This guide to Atonement features a biography of the author, a full-length analysis of the novel, a summary of the novel's popular and critical reception, a discussion of the recent film adaptation and a great deal more. If you are studying this novel, reading it for your book club, or if you simply want to know more about it, you'll find this guide informative, intelligent, and helpful.
For perhaps the first time in novel form, Benevolence presents an important era in Australia’s history from an Aboriginal perspective. Benevolence is told from the perspective of Darug woman, Muraging (Mary James), born around 1813. Mary’s was one of the earliest Darug generations to experience the impact of British colonisation. At an early age Muraging is given over to the Parramatta Native School by her Darug father. From here she embarks on a journey of discovery and a search for a safe place to make her home. The novel spans the years 1816-35 and is set around the Hawkesbury River area, the home of the Darug people, Parramatta and Sydney. The author interweaves historical events and characters — she shatters stereotypes and puts a human face to this Aboriginal perspective.
A moving true story that will pull at the heartstrings' - Woman & Home The first book in the Paws of Fame series, which follows movie animal trainer Julie Tottman as she rescues, nurtures and transforms animals in need of a second chance into film stars. Pickles the Yorkshire Terrier has just had her litter of puppies taken away from her - who knows how many litters she's delivered and watched the same thing happen to. She's been left behind in an overcrowded, noisy and dirty barn. She's very weak and her body is burning all over from a painful skin condition. This has been her life for six years and it will likely never change. Or will it? Julie is a young animal trainer for the movies and is looking for a Yorkshire Terrier for a new film she's working on with Colin Firth and Amanda Bynes. By chance, she hears of a puppy farm that has been raided by the authorities - the dogs were kept in appalling conditions and among them was a poor Yorkshire Terrier called Pickles. Julie doesn't know whether Pickles will be the right dog for the film, but she doesn't care: Pickles needs a safe home with love and care and Julie can give it. Will Pickles recover from the traumas of her past? Can she be the movie star Julie is looking for? And will Julie be able to make it in the world of movie animal trainers? Will You Take Me Home? is the moving true story of one woman and her dog. The second book in the Paws of Fame story - Rescue Me, about the abandoned Mastiff who went on to play Fang in Harry Potter - is available now
Generalized Linear Mixed Models: Modern Concepts, Methods, and Applications (2nd edition) presents an updated introduction to linear modeling using the generalized linear mixed model (GLMM) as the overarching conceptual framework. For students new to statistical modeling, this book helps them see the big picture – linear modeling as broadly understood and its intimate connection with statistical design and mathematical statistics. For readers experienced in statistical practice, but new to GLMMs, the book provides a comprehensive introduction to GLMM methodology and its underlying theory. Unlike textbooks that focus on classical linear models or generalized linear models or mixed models, this book covers all of the above as members of a unified GLMM family of linear models. In addition to essential theory and methodology, this book features a rich collection of examples using SAS® software to illustrate GLMM practice. This second edition is updated to reflect lessons learned and experience gained regarding best practices and modeling choices faced by GLMM practitioners. New to this edition are two chapters focusing on Bayesian methods for GLMMs. Key Features: • Most statistical modeling books cover classical linear models or advanced generalized and mixed models; this book covers all members of the GLMM family – classical and advanced models. • Incorporates lessons learned from experience and on-going research to provide up-to-date examples of best practices. • Illustrates connections between statistical design and modeling: guidelines for translating study design into appropriate model and in-depth illustrations of how to implement these guidelines; use of GLMM methods to improve planning and design. • Discusses the difference between marginal and conditional models, differences in the inference space they are intended to address and when each type of model is appropriate. • In addition to likelihood-based frequentist estimation and inference, provides a brief introduction to Bayesian methods for GLMMs. Walt Stroup is an Emeritus Professor of Statistics. He served on the University of Nebraska statistics faculty for over 40 years, specializing in statistical modeling and statistical design. He is a Fellow of the American Statistical Association, winner of the University of Nebraska Outstanding Teaching and Innovative Curriculum Award and author or co-author of three books on mixed models and their extensions. Marina Ptukhina (Pa-too-he-nuh), PhD, is an Associate Professor of Statistics at Whitman College. She is interested in statistical modeling, design and analysis of research studies and their applications. Her research includes applications of statistics to economics, biostatistics and statistical education. Ptukhina earned a PhD in Statistics from the University of Nebraska-Lincoln, a Master of Science degree in Mathematics from Texas Tech University and a Specialist degree in Management from The National Technical University "Kharkiv Polytechnic Institute." Julie Garai, PhD, is a Data Scientist at Loop. She earned her PhD in Statistics from the University of Nebraska-Lincoln and a bachelor’s degree in Mathematics and Spanish from Doane College. Dr Garai actively collaborates with statisticians, psychologists, ecologists, forest scientists, software engineers, and business leaders in academia and industry. In her spare time, she enjoys leisurely walks with her dogs, dance parties with her children, and playing the trombone.
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