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Vector Generalized Linear and Additive Models

With an Implementation in R

Autor: Thomas W. Yee

Limbă:

Legare: Carte broșată

Editura: Springer-Verlag New York Inc.

Disponibilitate: În depozitul extern

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593.54 lei

This book presents a greatly enlarged statistical framework compared to generalized linear models (G...

Informații despre carte

Autor

Thomas W. Yee

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2016

Pagini

589

EAN

9781493941988

ISBN

1493941984

Enbook ID

15178519

Editura

Springer-Verlag New York Inc.

Greutate

1077

Dimensiuni

178 x 254 x 25

Descriere completă

This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of statistical models, and fortified with necessary infrastructure to make the models more fully operable, the framework allows analyses based on many semi-traditional applied statistics models to be performed as a coherent whole. Since their advent in 1972, GLMs have unified important distributions under a single umbrella with enormous implications. However, GLMs are not flexible enough to cope with the demands of practical data analysis. And data-driven GLMs, in the form of generalized additive models (GAMs), are also largely confined to the exponential family. The methodology here and accompanying software (the extensive VGAM R package) are directed at these limitations and are described comprehensively for the first time in one volume. This book treats distributions and classical models as generalized regression models, and the result is a much broader application base for GLMs and GAMs. The book can be used in senior undergraduate or first-year postgraduate courses on GLMs or categorical data analysis and as a methodology resource for VGAM users. In the second part of the book, the R package VGAM allows readers to grasp immediately applications of the methodology. R code is integrated in the text, and datasets are used throughout. Potential applications include ecology, finance, biostatistics, and social sciences. The methodological contribution of this book stands alone and does not require use of the VGAM package.

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Vector Generalized Linear and Additive Models

With an Implementation in R

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Descriere completă

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