A01=Art B. Owen
advanced confidence region construction
Author_Art B. Owen
bartlett
Bartlett Correction
Bootstrap Calibration
Category=PBT
Category=PBW
censored data analysis
Confidence Regions
convex
Convex Hull
correction
Coverage Error
Cumulative Distribution Function
Empirical Likelihood
Empirical Likelihood Confidence
Empirical Likelihood Confidence Intervals
Empirical Likelihood Confidence Regions
Empirical Likelihood Function
Empirical Likelihood Inferences
Empirical Likelihood Ratio
Empirical Likelihood Ratio Function
Empirical Likelihood Test
Empirical Log Likelihood
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
equations
estimating
function
generalized linear models
hull
IID Sample
inferences
kernel smoothing applications
Likelihood Ratio
Likelihood Ratio Function
log
Nonparametric Likelihood
nonparametric statistics
Parametric Family
Parametric Likelihood
Parametric MLE
Profile Empirical Log Likelihood Ratio
Qq Plot
ratio
regression analysis techniques
statistical inference methods
Product details
- ISBN 9781584880714
- Weight: 720g
- Dimensions: 152 x 229mm
- Publication Date: 18 May 2001
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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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.
