Introduction

Abstract

The theory of estimating functions has become of interest in a wide variety of statistical applications, partly because it has a number of virtues in common with methods such as maximum likelihood estimation while possessing sufficient flexibility to tackle problems where maximum likelihood fails, such as the Neyman-Scott paradox. In this monograph we present a self-contained development with a number of applications to estimation, censoring, robustness and inferential separation of parameters.