This paper studies the method of empirical likelihood in models with weakly dependent
processes. In such cases, if the likelihood function is formulated as if the data process were …

A new approximation to the Gaussian likelihood of a multivariate locally stationary process is
introduced. It is based on an approximation of the inverse of the covariance matrix of such …

Empirical likelihood is developed for autoregressive models with innovations that form a
martingale difference sequence. Limiting distributions of the log empirical likelihood ratio …

In this paper, we consider the application of the empirical likelihood method to partially
linear model. Unlike the usual cases, we first propose an approximation to the residual of the …

We show that empirical likelihood is justified as a method of inference for a class of models
much larger than the class of linear models considered by Owen (1991). In particular, we …

A semiparametric regression model for longitudinal data is considered. The empirical
likelihood method is used to estimate the regression coefficients and the baseline function …

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 …

It is well known that we can use the likelihood ratio statistic to test hypotheses and to
construct confidence intervals in full parametric models. Recently, Owen introduced the …

Empirical likelihood is a popular nonparametric or semi-parametric statistical method with
many nice statistical properties. Yet when the sample size is small, or the dimension of the …

Two-sample empirical likelihood method Page 1 II ELSEVIER Statistics & Probability Letters 24
(1995) 315-319 STATISTICS& PROBABILITY LETTERS Two-sample empirical likelihood method …