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Linear Generalized Mixed Models by Jiang Jiming (43 results)
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Condition: New. pp. 276 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.
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Linear and Generalized Linear Mixed Models and Their Applications (Springer Series in Statistics)
Book 89 of 160: Springer Series in StatisticsSeller: World of Books (was SecondSale), Montgomery, IL, U.S.A.
Seller rating 5 out of 5 stars
Condition: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
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Linear and Generalized Linear Mixed Models and Their Applications (Springer Series in Statistics)
Book 157 of 160: Springer Series in StatisticsSeller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 4 out of 5 stars
hardcover. Condition: Very Good.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Linear and Generalized Linear Mixed Models and Their Applications
Book 157 of 160: Springer Series in StatisticsLanguage: English
Published by Springer (India) Private Limited, 2021
ISBN 10: 1071612816 ISBN 13: 9781071612811
Condition: New. pp. 357 2nd Edition NO-PA16APR2015-KAP.
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hardcover. Condition: New. In shrink wrap. Looks like an interesting title!
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Linear and Generalized Linear Mixed Models and Their Applications
Book 157 of 160: Springer Series in StatisticsLanguage: English
Published by Springer (India) Private Limited, 2021
ISBN 10: 1071612816 ISBN 13: 9781071612811
Condition: New. pp. 357.
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Linear and Generalized Linear Mixed Models and Their Applications
Book 157 of 160: Springer Series in StatisticsLanguage: English
Published by Springer (India) Private Limited, 2021
ISBN 10: 1071612816 ISBN 13: 9781071612811
Condition: New. pp. 357.
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Linear and Generalized Linear Mixed Models and Their Applications (Springer Series in Statistics)
Book 157 of 160: Springer Series in StatisticsSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 102.76
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Linear and Generalized Linear Mixed Models and Their Applications
Book 157 of 160: Springer Series in StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 100.64
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Linear and Generalized Linear Mixed Models and Their Applications (Springer Series in Statistics)
Book 157 of 160: Springer Series in StatisticsSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 106.31
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Condition: As New. Unread book in perfect condition.
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Linear and Generalized Linear Mixed Models and Their Applications (Springer Series in Statistics)
Book 89 of 160: Springer Series in StatisticsSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 116.47
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Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In English.
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Linear and Generalized Linear Mixed Models and Their Applications
Book 157 of 160: Springer Series in StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
£ 118.96
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Hardcover. Condition: Like New. Like New. book.
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Condition: New. 2nd ed. 2021 edition NO-PA16APR2015-KAP.
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Taschenbuch. Condition: Neu. Linear and Generalized Linear Mixed Models and Their Applications | Jiming Jiang (u. a.) | Taschenbuch | xiv | Englisch | 2022 | Springer | EAN 9781071612842 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Hardcover. Condition: Brand New. 2nd edition. 343 pages. 9.50x6.25x1.00 inches. In Stock.
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Hardcover. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Gebunden. Condition: New. Concentrates on two major classes of mixed effects models, linear mixed models and generalized linear mixed modelsOffers an up-to-date account of theory and methods in the analysis of these models as well as their applications in various fields.
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Paperback. Condition: Brand New. 271 pages. 9.00x6.00x0.63 inches. In Stock.
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Linear and Generalized Linear Mixed Models and Their Applications
Book 89 of 160: Springer Series in StatisticsLanguage: English
Published by Springer, Berlin, Springer, 2007
ISBN 10: 0387479414 ISBN 13: 9780387479415
Buch. Condition: Neu. Neuware - Over the past decade there has been an explosion of developments in mixed e ects models and their applications. This book concentrates on two major classes of mixed e ects models, linear mixed models and generalized linear mixed models, with the intention of o ering an up-to-date account of theory and methods in the analysis of these models as well as their applications in various elds. The rst two chapters are devoted to linear mixed models. We classify l- ear mixed models as Gaussian (linear) mixed models and non-Gaussian linear mixed models. There have been extensive studies in estimation in Gaussian mixed models as well as tests and con dence intervals. On the other hand, the literature on non-Gaussian linear mixed models is much less extensive, partially because of the di culties in inference about these models. However, non-Gaussian linear mixed models are important because, in practice, one is never certain that normality holds. This book o ers a systematic approach to inference about non-Gaussian linear mixed models. In particular, it has included recently developed methods, such as partially observed information, iterative weighted least squares, and jackknife in the context of mixed models. Other new methods introduced in this book include goodness-of- t tests, p- diction intervals, and mixed model selection. These are, of course, in addition to traditional topics such as maximum likelihood and restricted maximum likelihood in Gaussian mixed models.
