John A. Nelder's Books

Methods and Models in Statistics

In Honour of Professor John Nelder, FRS.

By: John A. Nelder,Niall M. Adams

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Methods and Models in Statistics

In Honour of Professor John Nelder, FRS.

By: John A. Nelder,Niall M. Adams

John Nelder is one of today''s leading statisticians, having made an impact on many parts of the discipline. This book contains reviews of some of those areas, written by top researchers. It is accessible to non-specialists, and is noteworthy for its breadth of coverage.

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Page Count

266

Publisher

Imperial College Press

Published Date

ISBN 10

1860945414

ISBN 13

9781860945410

Generalized Linear Models with Random Effects

Unified Analysis via H-likelihood, Second Edition

By: Youngjo Lee,John A. Nelder,Yudi Pawitan

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Generalized Linear Models with Random Effects

Unified Analysis via H-likelihood, Second Edition

By: Youngjo Lee,John A. Nelder,Yudi Pawitan

This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages added, including new material on the extended likelihood approach that strengthens the theoretical basis of the methodology, new developments in variable selection and multiple testing, and new examples and applications. It includes an R package for all the methods and examples that supplement the book.

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467

Publisher

CRC Press

Published Date

ISBN 10

1351646265

ISBN 13

9781351646260

Generalized Linear Models, Second Edition

By: P. McCullagh,John A. Nelder

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Generalized Linear Models, Second Edition

By: P. McCullagh,John A. Nelder

The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications. The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. They give particular emphasis to the important case where the dependence occurs through some unknown, linear combination of the explanatory variables. The Second Edition includes topics added to the core of the first edition, including conditional and marginal likelihood methods, estimating equations, and models for dispersion effects and components of dispersion. The discussion of other topics-log-linear and related models, log odds-ratio regression models, multinomial response models, inverse linear and related models, quasi-likelihood functions, and model checking-was expanded and incorporates significant revisions. Comprehension of the material requires simply a knowledge of matrix theory and the basic ideas of probability theory, but for the most part, the book is self-contained. Therefore, with its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference.

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Page Count

536

Publisher

CRC Press

Published Date

ISBN 10

0412317605

ISBN 13

9780412317606

Compact Numerical Methods for Computers

Linear Algebra and Function Minimisation

By: John C. Nash

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Compact Numerical Methods for Computers

Linear Algebra and Function Minimisation

By: John C. Nash

This second edition of Compact Numerical Methods for Computers presents reliable yet compact algorithms for computational problems. As in the previous edition, the author considers specific mathematical problems of wide applicability, develops approaches to a solution and the consequent algorithm, and provides the program steps. He emphasizes useful applicable methods from various scientific research fields, ranging from mathematical physics to commodity production modeling. While the ubiquitous personal computer is the particular focus, the methods have been implemented on computers as small as a programmable pocket calculator and as large as a highly parallel supercomputer. New to the Second Edition Presents program steps as Turbo Pascal code Includes more algorithmic examples Contains an extended bibliography The accompanying software (available by coupon at no charge) includes not only the algorithm source codes, but also driver programs, example data, and several utility codes to help in the software engineering of end-user programs. The codes are designed for rapid implementation and reliable use in a wide variety of computing environments. Scientists, statisticians, engineers, and economists who prepare/modify programs for use in their work will find this resource invaluable. Moreover, since little previous training in numerical analysis is required, the book can also be used as a supplementary text for courses on numerical methods and mathematical software.

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Page Count

298

Publisher

CRC Press

Published Date

ISBN 10

085274319X

ISBN 13

9780852743195

Regression

Linear Models in Statistics

By: N. H. Bingham,John M. Fry

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Regression

Linear Models in Statistics

By: N. H. Bingham,John M. Fry

Regression is the branch of Statistics in which a dependent variable of interest is modelled as a linear combination of one or more predictor variables, together with a random error. The subject is inherently two- or higher- dimensional, thus an understanding of Statistics in one dimension is essential. Regression: Linear Models in Statistics fills the gap between introductory statistical theory and more specialist sources of information. In doing so, it provides the reader with a number of worked examples, and exercises with full solutions. The book begins with simple linear regression (one predictor variable), and analysis of variance (ANOVA), and then further explores the area through inclusion of topics such as multiple linear regression (several predictor variables) and analysis of covariance (ANCOVA). The book concludes with special topics such as non-parametric regression and mixed models, time series, spatial processes and design of experiments. Aimed at 2nd and 3rd year undergraduates studying Statistics, Regression: Linear Models in Statistics requires a basic knowledge of (one-dimensional) Statistics, as well as Probability and standard Linear Algebra. Possible companions include John Haigh’s Probability Models, and T. S. Blyth & E.F. Robertsons’ Basic Linear Algebra and Further Linear Algebra.

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Page Count

293

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1848829698

ISBN 13

9781848829695

Book's by John A. Nelder

Methods and Models in Statistics

Methods and Models in Statistics

Generalized Linear Models with Random Effects

Generalized Linear Models with Random Effects

Generalized Linear Models, Second Edition

Generalized Linear Models, Second Edition

Compact Numerical Methods for Computers

Compact Numerical Methods for Computers

Regression

Regression

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