Generalized Linear Models, Robert Gilchrist; Brian Francis; Joe Whittaker

Автор: Alan Agresti
Название: Foundations of Linear and Generalized Linear Models
ISBN: 1118730038 ISBN-13(EAN): 9781118730034
Издательство: Wiley
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Цена: 16782.00 р.
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Описание: A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models.

Автор: Nachtsheim;Neter;Kutner
Название: Applied Linear Statistical Models with Student CD
ISBN: 0071122214 ISBN-13(EAN): 9780071122214
Издательство: McGraw-Hill
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Цена: 9265.00 р.
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Описание: "Applied Linear Statistical Models", 5e, is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.

Автор: Wood, Simon
Название: Generalized Additive Models: An Introduction with R
ISBN: 1584884746 ISBN-13(EAN): 9781584884743
Издательство: Taylor&Francis
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Цена: 10717.00 р.
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Описание: An Introduction to Generalized Additive Models with R provides readers with a thorough understanding of the theory and practical applications of GAMs to enable informed use of these very flexible tools and other advanced related models. The author's approach is based on a framework of penalized regression splines, and he provides a gentle introduction through motivating chapters on linear and generalized linear models. The author uses the freely available R software throughout to explain the underlying theory and illustrate the practicalities of linear, generalized linear, and generalized additive models. The text is accompanied by a supporting Web site that contains R code and the datasets used in the book.

Название: An Introduction to Generalized Linear Models, Third Edition
ISBN: 1584889500 ISBN-13(EAN): 9781584889502
Издательство: Taylor&Francis
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Цена: 7195.00 р.
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Описание: Offers a cohesive framework for statistical modeling. Emphasizing numerical and graphical methods, this work enables readers to understand the unifying structure that underpins GLMs. It discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, and longitudinal analysis.

Автор: Fox John
Название: Applied Regression Analysis and Generalized Linear Models
ISBN: 1452205663 ISBN-13(EAN): 9781452205663
Издательство: Sage Publications
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Цена: 25027.00 р.
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Описание: Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the most useful and widely used statistical tools for social research.

Автор: W. Hennevogl; Ludwig Fahrmeir; Gerhard Tutz
Название: Multivariate Statistical Modelling Based on Generalized Linear Models
ISBN: 1441929002 ISBN-13(EAN): 9781441929006
Издательство: Springer
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Цена: 27251.00 р.
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Описание: The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis. This second edition is extensively revised, especially those sections relating with Bayesian concepts.