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Maximum likelihood estimation and inference : with examples in R, SAS, and ADMB / Russell B. Millar.

By: Contributor(s): Material type: TextTextPublication details: Hoboken, N.J. : Wiley, ©2011.Description: pagesContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780470094846
  • 0470094842
Subject(s): Genre/Form: Additional physical formats: Print version:: Maximum likelihood estimation and inference.DDC classification:
  • 519.5/44 22
LOC classification:
  • QA276.8 .M55 2011
Online resources:
Contents:
Front Matter -- Preliminaries. A Taste of Likelihood -- Essential Concepts and Iid Examples -- Pragmatics. Hypothesis Tests and Confidence Intervals or Regions -- What you Really need to Know -- Maximizing the Likelihood -- Some Widely Used Applications of Maximum Likelihood -- Generalized Linear Models and Extensions -- Quasi-Likelihood and Generalized Estimating Equations -- ML Inference in the Presence of Incidental Parameters -- Latent Variable Models -- Theoretical Foundations. Cram̌r-Rao Inequality and Fisher Information -- Asymptotic Theory and Approximate Normality -- Tools of the Trade -- Fundamental Paradigms and Principles of Inference -- Miscellanea -- Appendix: Partial Solutions to Selected Exercises -- Bibliography -- Index -- Statistics in Practice.
Summary: "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. The methods are implemented in SAS--the most widely used statistical software package--and the data sets and SAS code are provided on a Web site, enabling the reader to use the methods to solve problems in their own work. This book serves as an ideal text for applied scientists and researchers and graduate students of statistics"-- Provided by publisher.Summary: "This book is the first to provide an accessible and practical introduction to likelihood modeling, supported by examples and software, and is suitable for the applied scientist"-- Provided by publisher.
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Front Matter -- Preliminaries. A Taste of Likelihood -- Essential Concepts and Iid Examples -- Pragmatics. Hypothesis Tests and Confidence Intervals or Regions -- What you Really need to Know -- Maximizing the Likelihood -- Some Widely Used Applications of Maximum Likelihood -- Generalized Linear Models and Extensions -- Quasi-Likelihood and Generalized Estimating Equations -- ML Inference in the Presence of Incidental Parameters -- Latent Variable Models -- Theoretical Foundations. Cram̌r-Rao Inequality and Fisher Information -- Asymptotic Theory and Approximate Normality -- Tools of the Trade -- Fundamental Paradigms and Principles of Inference -- Miscellanea -- Appendix: Partial Solutions to Selected Exercises -- Bibliography -- Index -- Statistics in Practice.

"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. The methods are implemented in SAS--the most widely used statistical software package--and the data sets and SAS code are provided on a Web site, enabling the reader to use the methods to solve problems in their own work. This book serves as an ideal text for applied scientists and researchers and graduate students of statistics"-- Provided by publisher.

"This book is the first to provide an accessible and practical introduction to likelihood modeling, supported by examples and software, and is suitable for the applied scientist"-- Provided by publisher.

Includes bibliographical references and index.

Electronic reproduction. Hoboken, N.J. : Wiley InterScience, 2011. Mode of access: World Wide Web. System requirements: Web browser. Title from title screen (viewed on Aug. 2, 2011). Access may be restricted to users at subscribing institutions.

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