Empirical Likelihood. EL for Random Vectors. Regression and Modeling. Symmetry and Independence. Imperfectly Observed Data. Curve Estimation. Dependent Data. Hybrids and Connections. Some Proofs. Algorithms. Higher Order Asymptotics.
Owen, Art B.
"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
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