Russell B Millar (15 results)

Hardcover. Condition: Fine. 1st Edition. Like new condition, clean inside and out, no writing or highlighting on pages.

Hardcover. Condition: Very Good. 1st Edition. Hardcover, xvi + 357 pages, NOT ex-library. Book is clean and bright with unmarked text, free of inscriptions and stamps, firmly bound. Minor handling wear only. Issued without a dust jacket. -- This practical guide to modern statistical modelling focuses on the application of maximum likelihood (ML) principles, providing researchers with the tools to analyze real data across disciplines such as biology, medicine, and ecology. The book is structured to lead readers from foundational concepts to advanced practical implementation, specifically utilizing R, SAS, and ADMB (Automatic Differentiation Model Builder) software. Part I: Preliminaries introduces the notation and logic of likelihood, using simple examples like the binomial and normal distributions to build intuition. Part II: Pragmatics focuses on "what you really need to know" for daily practice, including hypothesis testing (Wald and Likelihood Ratio tests), confidence intervals, and model selection. Part III: Theoretical Foundations reserves more formal statistical theory - such as the Cramér-Rao inequality, Fisher information, and asymptotic normality - for the final chapters to keep the earlier sections accessible and pragmatic. A distinguishing feature is the inclusion of annotated code for three major platforms. It is particularly noted for its introduction to ADMB, a tool highly valued for complex non-linear models. Beyond basic estimation, the text covers complex scenarios like Latent Variable Models, Generalized Linear Models (GLMs), and Quasi-likelihood. It details specific applications like Box-Cox transformations, survival-time data models, and mark-recapture models used in ecological field studies. It explains the algorithms behind the scenes, such as Newton-Raphson and the Expectation-Maximization (EM) algorithm, which are essential for finding maximum likelihood solutions when analytical ones are not available. According to reviewers from the International Statistical Review, the book is well-suited for applied scientists and graduate students who use likelihood methods in their own research.…

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: new.

Condition: New. In English.

Hardback. Condition: New. This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm. Key features: Provides an accessible introduction to pragmatic maximum likelihood modelling.Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood.Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data.Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology.Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB.Provides all program code and software extensions on a supporting website.Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters.    This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.…

Condition: New. pp. xvi + 357 Illus.

Condition: New. Applied Likelihood Methods provides an accessible and practical introduction to likelihood modeling, supported by examples and software. The book features applications from a range of disciplines, including statistics, medicine, biology, and ecology. Series: Statistics in Practice. Num Pages: 376 pages, Illustrations. BIC Classification: PBT. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 157 x 236 x 24. Weight in Grams: 664. . 2011. 1st Edition. Hardcover. . . . . …

Condition: New.

Condition: New. pp. xvi + 357.

Condition: New. Applied Likelihood Methods provides an accessible and practical introduction to likelihood modeling, supported by examples and software. The book features applications from a range of disciplines, including statistics, medicine, biology, and ecology. Series: Statistics in Practice. Num Pages: 376 pages, Illustrations. BIC Classification: PBT. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 157 x 236 x 24. Weight in Grams: 664. . 2011. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland. …

Hardback. Condition: New. This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm. Key features: Provides an accessible introduction to pragmatic maximum likelihood modelling.Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood.Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data.Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology.Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB.Provides all program code and software extensions on a supporting website.Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters.    This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.…

Condition: New.

Hardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Hardcover. Condition: new. Hardcover. This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm. Key features: Provides an accessible introduction to pragmatic maximum likelihood modelling.Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood.Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data.Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology.Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB.Provides all program code and software extensions on a supporting website.Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters. This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter. Applied Likelihood Methods provides an accessible and practical introduction to likelihood modeling, supported by examples and software. The book features applications from a range of disciplines, including statistics, medicine, biology, and ecology. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…