Author Description

The area of estimating functions has wide applications and hasundergone rapid development during the recent years. As a theory, itis sufficiently general to include most of the important aspects ofthe classical theory of statistical inference and to accomodate avariety of the more recent themes such as the Generalized LinearModels (GLM), the Generalized Estimating Equations (GEE), theGeneralized Linear Mixed Effect Models (GLMM), the various forms ofAutoregressive Conditionally Heteroscedastic models (ARCH), theRestrictive Maximum Likelihood (REML), the Empirical Likelihood, aswell as many estsimators in Surivval Analysis, NonparametricRegression, and Spatial Statistics. By the constructive nature of thistheory, it will no doubt also provide us with the insights to derivenew statistical procedures for scientific problems that will arise.Yet, these advantages do not make the subject more difficult tounderstand, for it is built on a handful of elementary tools such aslinearization and projection, which apply repeatedly at differentlevels of sophistication.