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LINEAR AND GENERALIZED LINEAR MIXED MODELS AND THEIR APPLICATIONS 2ED (HB 2021)

LINEAR AND GENERALIZED LINEAR MIXED MODELS AND THEIR APPLICATIONS 2ED (HB 2021)

LINEAR AND GENERALIZED LINEAR MIXED MODELS AND THEIR APPLICATIONS 2ED (HB 2021)
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LINEAR AND GENERALIZED LINEAR MIXED MODELS AND THEIR APPLICATIONS 2ED (HB 2021) Hardback - 2021

by JIANG J

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USA Edition . New. Brand New! Fast Delivery US Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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  • Title LINEAR AND GENERALIZED LINEAR MIXED MODELS AND THEIR APPLICATIONS 2ED (HB 2021)
  • Author JIANG J
  • Binding Hardback
  • Edition USA Edition
  • Condition New
  • Pages 343
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 2021-03-23
  • Illustrated Yes
  • Features Illustrated
  • Bookseller's Inventory # CVS 9781071612811
  • ISBN 9781071612811 / 1071612816
  • Weight 1.5 lbs (0.68 kg)
  • Dimensions 9.21 x 6.14 x 0.81 in (23.39 x 15.60 x 2.06 cm)
  • Category Medical / Nursing
  • Quantity available 1

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From the publisher

Now in its second edition, this book covers two major classes of mixed effects models--linear mixed models and generalized linear mixed models--and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.

This book is suitable for students, researchers, and practitioners who are interested in using mixed models for statistical data analysis with public health applications. It is best for graduate courses in statistics, or for those who have taken a first course in mathematical statistics, are familiar with using computers for data analysis, and have a foundational background in calculus and linear algebra.

From the rear cover

Now in its second edition, this book covers two major classes of mixed effects models--linear mixed models and generalized linear mixed models--and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.

This book is suitable for students, researchers, and practitioners who are interested in using mixed models for statistical data analysis with public health applications. It is best for graduatecourses in statistics, or for those who have taken a first course in mathematical statistics, are familiar with using computers for data analysis, and have a foundational background in calculus and linear algebra.

About the author

Jiming Jiang is Professor of Statistics and a former Director of Statistical Laboratory at the University of California, Davis. He is a prominent researcher in the fields of mixed effects models, small area estimation, model selection, and statistical genetics. He is the author of Large Sample Techniques for Statistics (Springer 2010), Robust Mixed Model Analysis (2019), Asymptotic Analysis of Mixed Effects Models: Theory, Applications, and Open Problems (2017), and The Fence Methods (with T. Nguyen, 2016). He has been editorial board member of The Annals of Statistics and Journal of the American Statistical Association, among others. He is a Fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics; an elected member of the International Statistical Institute; and a Yangtze River Scholar (Chaired Professor, 2017-2020).

Thuan Nguyen is Associate Professor of Biostatistics in the School of Public Health at Oregon Health & Science University, where she teaches and advises graduate students. She is an active researcher in the field of biostatistics, specializing in the analysis of longitudinal data and statistical genetics, as well as small area estimation. She is the coauthor of The Fence Methods (with J. Jiang 2016).

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