Empirical Likelihood

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.