Empirical likelihood allows the statistician
to employ likelihood methods, without having
to pick a parametric family for the data.
It is described in a
monograph
published by Chapman and Hall/CRC Press (ISBN 1584880716).
The table of contents is given here in
PostScript
and in
PDF .
There is a large and growing literature extending
empirical likelihood methods to many statistical
problems. A partial bibliography (as of May 2000) is given below.
The book can be ordered online from the usual places. |

"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**

PDF of documentation

with an emphasis on econometric applications

Example from Biology |
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Example from Physics |
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Example from Econometrics |