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

By: Contributor(s): Material type: TextTextSeries: Statistics in practicePublication details: Chichester, Sussex, U.K. ; Hoboken, N.J. : Wiley, 2011.Description: xvi, 357 p. : illSubject(s): Genre/Form: DDC classification:
  • 519.5/44 22
LOC classification:
  • QA276.8 .M55 2011eb
Online resources:
Contents:
pt. 1. Preliminaries -- pt. 2. Pragmatics -- pt. 3. Theoretical foundations.
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.
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Includes bibliographical references and index.

pt. 1. Preliminaries -- pt. 2. Pragmatics -- pt. 3. Theoretical foundations.

"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.

Electronic reproduction. Palo Alto, Calif. : ebrary, 2013. Available via World Wide Web. Access may be limited to ebrary affiliated libraries.

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