Adjusted Empirical Likelihood and its Properties

Adjusted empirical likelihood and its properties

Computing profile empirical likelihood function is a key step in applications of empirical likelihood which involves constrained maximization. However, in some situations, solutions to the corresponding constraints may not exist. In this case, the convention is to assign a zero value to the profile empirical likelihood. This convention has at least two limitations. First, it is numerically diff...

Empirical Likelihood Approach and its Application on Survival Analysis

A number of nonparametric methods exist when studying the population and its parameters in the situation when the distribution is unknown. Some of them such as "resampling bootstrap method" are based on resampling from an initial sample. In this article empirical likelihood approach is introduced as a nonparametric method for more efficient use of auxiliary information to construct...

Adjusted Empirical Likelihood Method for Quantiles

Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators

In an effort to improve the small sample properties of generalized method of moments (GMM) estimators, a number of alternative estimators have been suggested. These include empirical likelihood (EL), continuous updating, and exponential tilting estimators. We show that these estimators share a common structure, being members of a class of generalized empirical likelihood (GEL) estimators. We us...

Weighted empirical likelihood estimates and their robustness properties

Maximum likelihood methods are by far the most popular methods for deriving statistical estimators. However, parametric likelihoods require distributional specifications. The empirical likelihood is a nonparametric likelihood function that does not require such distributional assumptions, but is otherwise analogous to its parametric counterpart. Both likelihoods assume that the random variables...