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Record Nr. |
UNINA9910484963903321 |
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Autore |
Jiang Jiming |
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Titolo |
Linear and Generalized Linear Mixed Models and Their Applications / / by Jiming Jiang, Thuan Nguyen |
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Pubbl/distr/stampa |
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New York, NY : , : Springer New York : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[2nd ed. 2021.] |
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Descrizione fisica |
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1 online resource (352 pages) : illustrations |
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Collana |
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Springer Series in Statistics, , 2197-568X |
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Disciplina |
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Soggetti |
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Biometry |
Probabilities |
Statistics |
Public health |
Numerical analysis |
Population genetics |
Biostatistics |
Probability Theory |
Statistical Theory and Methods |
Public Health |
Numerical Analysis |
Population Genetics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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1. Linear Mixed Models: Part I -- 2. Linear Mixed Models: Part II -- 3. Generalized Linear Mixed Models: Part I -- 4. Generalized Linear Mixed Models: Part II. |
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Sommario/riassunto |
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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 |
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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. |
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