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The Resource Linear and generalized linear mixed models and their applications, Jiming Jiang

Linear and generalized linear mixed models and their applications, Jiming Jiang

Label
Linear and generalized linear mixed models and their applications
Title
Linear and generalized linear mixed models and their applications
Statement of responsibility
Jiming Jiang
Creator
Subject
Language
eng
Summary
"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. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it has included recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models." "The book is aimed at students, researchers and other practitioners who are interested in using mixed models for statistical data analysis. The book is suitable for a course in a M.S. program in statistics, provided that the section of further results and technical notes in each of the first four chapters is skipped. If these four sections are included, the book may be used for a course in a Ph.D. program in statistics. A first course in mathematical statistics, the ability to use computers for data analysis, and familiarity with calculus and linear algebra are prerequisites. Additional statistical courses such as regression analysis and a good knowledge about matrices would be helpful."--BOOK JACKET
Member of
Cataloging source
UKM
http://library.link/vocab/creatorName
Jiang, Jiming
Dewey number
519.535
Illustrations
illustrations
Index
index present
LC call number
QA276
LC item number
.J456 2007
Literary form
non fiction
Nature of contents
bibliography
Series statement
Springer series in statistics
http://library.link/vocab/subjectName
  • Mathematical statistics
  • Linear models (Statistics)
  • Linear Models
Label
Linear and generalized linear mixed models and their applications, Jiming Jiang
Instantiates
Publication
Bibliography note
Includes bibliographical references (pages [241]-253) and index
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Cover -- Contents -- Preface -- 1 Linear Mixed Models: Part I -- 1.1 Introduction -- 1.1.1 Effect of Air Pollution Episodes on Children -- 1.1.2 Prediction of Maize Single-Cross Performance -- 1.1.3 Small Area Estimation of Income -- 1.2 Types of Linear Mixed Models -- 1.2.1 Gaussian Mixed Models -- 1.2.2 Non-Gaussian Linear Mixed Models -- 1.3 Estimation in Gaussian Models -- 1.3.1 Maximum Likelihood -- 1.3.2 Restricted Maximum Likelihood -- 1.4 Estimation in Non-Gaussian Models -- 1.4.1 Quasi-Likelihood Method -- 1.4.2 Partially Observed Information -- 1.4.3 Iterative Weighted Least Squares -- 1.4.4 Jackknife Method -- 1.5 Other Methods of Estimation -- 1.5.1 Analysis of Variance Estimation -- 1.5.2 Minimum Norm Quadratic Unbiased Estimation -- 1.6 Notes on Computation and Software -- 1.6.1 Notes on Computation -- 1.6.2 Notes on Software -- 1.7 Real-Life Data Examples -- 1.7.1 Analysis of Birth Weights of Lambs -- 1.7.2 Analysis of Hip Replacements Data -- 1.8 Further Results and Technical Notes -- 1.9 Exercises -- 2 Linear Mixed Models: Part II -- 2.1 Tests in Linear Mixed Models -- 2.1.1 Tests in Gaussian Mixed Models -- 2.1.2 Tests in Non-Gaussian Linear Mixed Models -- 2.2 Confidence Intervals in Linear Mixed Models -- 2.2.1 Confidence Intervals in Gaussian Mixed Models -- 2.2.2 Confidence Intervals in Non-Gaussian Linear Mixed Models -- 2.3 Prediction -- 2.3.1 Prediction of Mixed Effect -- 2.3.2 Prediction of Future Observation -- 2.4 Model Checking and Selection -- 2.4.1 Model Diagnostics -- 2.4.2 Model Selection -- 2.5 Bayesian Inference -- 2.5.1 Inference about Variance Components -- 2.5.2 Inference about Fixed and Random Effects -- 2.6 Real-Life Data Examples -- 2.6.1 Analysis of the Birth Weights of Lambs (Continued) -- 2.6.2 The Baseball Example -- 2.7 Further Results and Technical Notes -- 2.8 Exercises -- 3 Generalized Linear Mixed Models: Part I -- 3.1 Introduction -- 3.2 Generalized Linear Mixed Models -- 3.3 Real-Life Data Examples -- 3.3.1 The Salamander Mating Experiments -- 3.3.2 A Log-Linear Mixed Model for Seizure Counts -- 3.3.3 Small Area Estimation of Mammography Rates -- 3.4 Likelihood Function under GLMM -- 3.5 Approximate Inference -- 3.5.1 Laplace Approximation -- 3.5.2 Penalized Quasi-Likelihood Estimation -- 3.5.3 Tests of Zero Variance Components -- 3.5.4 Maximum Hierarchical Likelihood -- 3.6 Prediction of Random Effects -- 3.6.1 Joint Estimation of Fixed and Random Effects -- 3.6.2 Empirical Best Prediction -- 3.6.3 A Simulated Example -- 3.7 Further Results and Technical Notes -- 3.7.1 More on NLGSA -- 3.7.2 Asymptotic Properties of PQWLS Estimators -- 3.7.3 MSE of EBP -- 3.7.4 MSPE of the Model-Assisted EBP -- 3.8 Exercises -- 4 Generalized Linear Mixed Models: Part II -- 4.1 Likelihood-Based Inference -- 4.1.1 A Monte Carlo EM Algorithm for Binary Data -- 4.1.2 Extensions -- 4&#
Control code
77256604
Dimensions
24 cm
Extent
xiv, 257 pages
Isbn
9780387479415
Isbn Type
(acid-free paper)
Lccn
2006935876
Media category
unmediated
Media MARC source
rdamedia
Media type code
n
Other physical details
illustrations
System control number
(OCoLC)77256604
Label
Linear and generalized linear mixed models and their applications, Jiming Jiang
Publication
Bibliography note
Includes bibliographical references (pages [241]-253) and index
Carrier category
volume
Carrier category code
nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Cover -- Contents -- Preface -- 1 Linear Mixed Models: Part I -- 1.1 Introduction -- 1.1.1 Effect of Air Pollution Episodes on Children -- 1.1.2 Prediction of Maize Single-Cross Performance -- 1.1.3 Small Area Estimation of Income -- 1.2 Types of Linear Mixed Models -- 1.2.1 Gaussian Mixed Models -- 1.2.2 Non-Gaussian Linear Mixed Models -- 1.3 Estimation in Gaussian Models -- 1.3.1 Maximum Likelihood -- 1.3.2 Restricted Maximum Likelihood -- 1.4 Estimation in Non-Gaussian Models -- 1.4.1 Quasi-Likelihood Method -- 1.4.2 Partially Observed Information -- 1.4.3 Iterative Weighted Least Squares -- 1.4.4 Jackknife Method -- 1.5 Other Methods of Estimation -- 1.5.1 Analysis of Variance Estimation -- 1.5.2 Minimum Norm Quadratic Unbiased Estimation -- 1.6 Notes on Computation and Software -- 1.6.1 Notes on Computation -- 1.6.2 Notes on Software -- 1.7 Real-Life Data Examples -- 1.7.1 Analysis of Birth Weights of Lambs -- 1.7.2 Analysis of Hip Replacements Data -- 1.8 Further Results and Technical Notes -- 1.9 Exercises -- 2 Linear Mixed Models: Part II -- 2.1 Tests in Linear Mixed Models -- 2.1.1 Tests in Gaussian Mixed Models -- 2.1.2 Tests in Non-Gaussian Linear Mixed Models -- 2.2 Confidence Intervals in Linear Mixed Models -- 2.2.1 Confidence Intervals in Gaussian Mixed Models -- 2.2.2 Confidence Intervals in Non-Gaussian Linear Mixed Models -- 2.3 Prediction -- 2.3.1 Prediction of Mixed Effect -- 2.3.2 Prediction of Future Observation -- 2.4 Model Checking and Selection -- 2.4.1 Model Diagnostics -- 2.4.2 Model Selection -- 2.5 Bayesian Inference -- 2.5.1 Inference about Variance Components -- 2.5.2 Inference about Fixed and Random Effects -- 2.6 Real-Life Data Examples -- 2.6.1 Analysis of the Birth Weights of Lambs (Continued) -- 2.6.2 The Baseball Example -- 2.7 Further Results and Technical Notes -- 2.8 Exercises -- 3 Generalized Linear Mixed Models: Part I -- 3.1 Introduction -- 3.2 Generalized Linear Mixed Models -- 3.3 Real-Life Data Examples -- 3.3.1 The Salamander Mating Experiments -- 3.3.2 A Log-Linear Mixed Model for Seizure Counts -- 3.3.3 Small Area Estimation of Mammography Rates -- 3.4 Likelihood Function under GLMM -- 3.5 Approximate Inference -- 3.5.1 Laplace Approximation -- 3.5.2 Penalized Quasi-Likelihood Estimation -- 3.5.3 Tests of Zero Variance Components -- 3.5.4 Maximum Hierarchical Likelihood -- 3.6 Prediction of Random Effects -- 3.6.1 Joint Estimation of Fixed and Random Effects -- 3.6.2 Empirical Best Prediction -- 3.6.3 A Simulated Example -- 3.7 Further Results and Technical Notes -- 3.7.1 More on NLGSA -- 3.7.2 Asymptotic Properties of PQWLS Estimators -- 3.7.3 MSE of EBP -- 3.7.4 MSPE of the Model-Assisted EBP -- 3.8 Exercises -- 4 Generalized Linear Mixed Models: Part II -- 4.1 Likelihood-Based Inference -- 4.1.1 A Monte Carlo EM Algorithm for Binary Data -- 4.1.2 Extensions -- 4&#
Control code
77256604
Dimensions
24 cm
Extent
xiv, 257 pages
Isbn
9780387479415
Isbn Type
(acid-free paper)
Lccn
2006935876
Media category
unmediated
Media MARC source
rdamedia
Media type code
n
Other physical details
illustrations
System control number
(OCoLC)77256604

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