Empirical likelihood is widely used in many statistical problems. In this article, we provide a
review of the empirical likelihood method, due to its significant development in recent years …

This book has been developed out of our many years of teaching an advanced course in
survey sampling to fourth-year undergraduate students and graduate students in statistics …

It is common to have access to summary information from external studies. Such information
can be useful for an internal study of interest to improve parameter estimation efficiency …

We study a marginal empirical likelihood approach in scenarios when the number of
variables grows exponentially with the sample size. The marginal empirical likelihood ratios …

Nonignorable missing data are frequently encountered in various settings, such as
economics, sociology and biomedicine. We review statistical inference for nonignorable …

Ridge and lasso regression models, which are also known as regularization methods, are
widely used methods in machine learning and inverse problems that introduce additional …

Statistical methods with empirical likelihood (EL) are appealing and effective especially in
conjunction with estimating equations for flexibly and adaptively incorporating data …

Efficient estimation of linear functionals of principal components Page 1 The Annals of
Statistics 2020, Vol. 48, No. 1, 464–490 https://doi.org/10.1214/19-AOS1816 © Institute of …

This paper considers the maximum generalized empirical likelihood (GEL) estimation and
inference on parameters identified by high dimensional moment restrictions with weakly …

With the increasing availability of data in the public domain, there has been a growing
interest in exploiting information from external sources to improve the analysis of smaller …