Empirical Likelihood Home Page

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.


"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

Errata: PostScript | PDF Bibliography: PostScript | PDF


scel.R Posted Mar 2015
R function to compute empirical likelihood using a self-concordant convex criterion. Thanks to Leo Belzile for spotting an error in the hpp function, which is fixed in the linked code. That did not affect computation in the region of the solution, but might have affected progress towards the solution.

scelcount.R Posted Feb 2017
PDF of documentation
This is a rewrite of the R function above to handle data with lots of ties. The input is a matrix of values and a vector of count where the i'th count is the number of times the i'th row of the matrix appears as data. It is meant to handle cases such as zero inflated counts so the user does not have to store all those zeros.

Justin Grana's python code for descriptive statistics by empirical likelihood
Justin Grana's python code for regression by empirical likelihood
Justin Grana's python code for regression confidence intervals
Splus functions to calculate empirical likelihood for a (vector) mean. (Free with no gaurantees.) Also outdated vs scel.R.
John Zedlewski's Matlab code
with an emphasis on econometric applications
Mai Zhou's R code for empirical likelihood, with an emphasis on survival analysis.
elm.m plog.m
Matlab functions to calculate empirical likelihood for a (vector) mean.
Hansen's Gauss
Bruce Hansen's Gauss language code for EL and GMM


Empirical likelihood is of interest to econometricians, because it offers some advantages over GMM (generalized method of moments). Readers seeking to follow up with applications of empirical likelihood to econometrics, should turn to Econometric Foundations

Empirical Likelihood Researchers

Gianfranco Adimari University of Padua
Murray Aitkin University of Newcastle
Keith Baggerly Rice University
Francesco Bartolucci University of Perugia
Jiahua Chen University of Waterloo
Song-Xi Chen National University of Singapore
Chin-Shan Chuang University of Wisconsin
Nidhan Choudhuri Case Western Reserve University
Tom DiCiccio Cornell University
Kim-Ahn Do M.D. Anderson Cancer Center
Hammou El Barmi Kansas State University
Jianqing Fan University of California, Los Angeles
Kostas Fokianos University of Cyprus
Nancy Glenn Rice University
Peter Hall Australian National University
Bruce Hansen University of Wisconsin
Tim Hesterberg Google Inc.
Myles Hollander Florida State University
Guido Imbens University of California, Los Angeles
George Judge University of California, Berkeley
Yuichi Kitamura University of Wisconsin
Eric Kolaczyk Boston University
Jerry Lawless University of Waterloo
Wei-Liem Loh National University of Singapore
Susan Murphy University of Michigan
Nicole Lazar Carnegie-Mellon University
Ian McKeague Florida State University
Per Mykland University of Chicago
Whitney Newey M.I.T.
Yudi Pawitan University College, Cork, Ireland
Liang Peng Georgia Tech
Brett Presnell University of Florida
Jing Qin Memorial Sloan-Kettering Institute
Jon Rao Carleton University
Jian-Jian Ren Tulane University
Joe Romano Stanford University
Xiaotong Shen Ohio State University
Richard Smith University of Bristol
Randy Sitter Simon Fraser University
Richard Spady Oxford University
Min Tsao University of Victoria
Ingrid van Keilegom Universite Catholiue de Louvain
Aad van der Vaart The Vrije Universiteit, Amsterdam
Qihua Wang AMSS, China
Brian Yandell University of Wisconsin
Biao Zhang University of Toledo
Fei Zou University of Wisconsin
Yichuan Zhao Georgia State University

Some Illustrations of Empirical Likelihood

Example from Biology
Example from Physics
Example from Econometrics