Empirical Likelihood [Hardcover]

Art B. Owen (Author)

Book Description

ISBN-10: 1584880716 | ISBN-13: 978-1584880714 | Publication Date: May 18, 2001 | Edition: 1

Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. It also facilitates incorporating side information, and it simplifies accounting for censored, truncated, or biased sampling.

One of the first books published on the subject, Empirical Likelihood offers an in-depth treatment of this method for constructing confidence regions and testing hypotheses. The author applies empirical likelihood to a range of problems, from those as simple as setting a confidence region for a univariate mean under IID sampling, to problems defined through smooth functions of means, regression models, generalized linear models, estimating equations, or kernel smooths, and to sampling with non-identically distributed data. Abundant figures offer visual reinforcement of the concepts and techniques. Examples from a variety of disciplines and detailed descriptions of algorithms-also posted on a companion Web site at-illustrate the methods in practice. Exercises help readers to understand and apply the methods.

The method of empirical likelihood is now attracting serious attention from researchers in econometrics and biostatistics, as well as from statisticians. This book is your opportunity to explore its foundations, its advantages, and its application to a myriad of practical problems.

Editorial Reviews

Review

In this beautifully written book Owen lucidly illustrates the wide applicability of empirical likelihood and provides masterful accounts of its latest theoretical developments. Numerous empirical examples should fascinate practitioners in various fields of science. I recommend this book extremely highly.
-Yuichi Kitamura, Department of Economics, University of Pennsylvania

The statistical model discovery and information recovery process is shrouded in a great deal of uncertainty. Owen's empirical likelihood procedure provides an attractive basis for how best to represent the sampling process and to carry through the estimation and inference objectives
- George Judge, University of California, Berkeley

A great amount of thought and care has gone into preparing this fascinating monograph. Empirical likelihood is somehow at the junction between two of the main streams of contemporary statistics, parametric and nonparametric methods. Through EL, some of the key results of the former (such as Wilks' Theorem and Bartlett correctibility) carry over to the latter in a way which seems almost to deny the infinite-parameter character of nonparametric statistics. Even if the purpose of empirical likelihood was no more than this didactic one, it would be significant. Yet as Owen shows so engagingly, EL also has a colourful life of its own. It is a unique practical tool, and it enjoys important, and growing, connections to many areas of statistics, from the Kaplan-Meier estimator to the bootstrap and beyond. If we look at statistics from the vantage point of EL we can see a long way; Owen shows us how, and how far.
-Professor Peter Hall, Australian National University.

This impressive monograph is the definitive source for researchers who wish to learn how to utilize empirical likelihood methods. The author addresses a range of topics, including univariate confidence intervals, regression models, kernel smoothing, and mean function smoothing. Although the book covers considerable ground and is rigorous, the book is well written and a reader with a solid background in mathematical statistics can readily tackle this volume.
-Journal of Mathematical Psychology

This book will make accessible to a wider audience the new and important area of nonparameteric likelihood and hypothesis testing. Masterfully written by a pioneer in this area, this book lucidly discusses the statistical theory and -- perhaps more importantly for applied econometricians -- computational details and practical aspects of putting the ideas to work with real data. This book will have a major impact on the way hypothesis testing is done in econometrics, where one is very often unsure about what the correct model specification is.
-Anand V. Bodapati, UCLA Anderson School of Management, USA

The book will make an ideal text for a course in empirical likelihood for advanced statistics students, while it provides theoretically-minded practitioners a quick access to the growing empirical likelihood literature... The writing style is extremely clear throughout, even when discussing the fine points of the theory. Important results are well motivated, discussed and illustrated by real data examples.
-Biometrics, vol. 57, no. 4, December 2001

Product Details

• Hardcover: 320 pages
• Publisher: Chapman and Hall/CRC; 1 edition (May 18, 2001)
• Language: English
• ISBN-10: 1584880716
• ISBN-13: 978-1584880714
• Product Dimensions: 9 x 6.2 x 0.9 inches
• Shipping Weight: 1.2 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #1,157,505 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

14 of 15 people found the following review helpful:

5.0 out of 5 stars Destined to be a classic, August 22, 2001

By 

Anand V. Bodapati (Los Angeles, CA United States) - See all my reviews

This review is from: Empirical Likelihood (Hardcover)

This book will make accessible to a wider audience the new and important area of nonparameteric likelihood and hypothesis testing. It is written by a pioneer in this area. The ideas in this book are very sophisticated but one has no difficulty following the arguments because the writing is crystal clear; the author must have put in a lot of thought into the organization and presentation of the ideas. The book lucidly discusses the statistical theory and -- perhaps more importantly for the practitioner -- computational details and practical aspects of putting the ideas to work with real data. I expect that this book will have a major impact on the way hypothesis testing is done in econometrics and other areas of applied statistics, where one is very often unsure about what the correct model specification is and cannot rely on certain large sample approximations. A terrific book. I would recommend it very highly to practitioners in areas where hypothesis testing is too important to be left to less correct approaches (I am thinking of areas like biostatistics, quality control, econometrics, but I expect there are many such areas).