Empirical Likelihood
By addebook • Jul 14th, 2008 • Category: Mathematics •
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Empirical Likelihood
Empirical Likelihood
By Art B. Owen
Publisher: Chapman & Hall/CRC
Number Of Pages: 320
Publication Date: 2001-05-18
ISBN-10 / ASIN: 1584880716
ISBN-13 / EAN: 9781584880714
Binding: Hardcover
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.
Summary: Destined to be a classic
Rating: 5
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).
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