Variance reduction in multiparameter likelihood models
Journal
Journal of the American Statistical Association
Journal Volume
102
Journal Issue
477
Pages
293-304
Date Issued
2007
Author(s)
Abstract
Local likelihood modeling is a unified and effective approach to establishing the dependence of a response variable, which can be of various types, on independent variables. Therefore, these models have become popular in a wide range of applications. There is an increasing . interest in employing multiparameter local likelihood models to investigate trends of sample extremes in environmental statistics. When sample maxima are modeled by a generalized extreme value distribution, the sample size is small in general and local likelihood estimation exhibits a large variation. In this article variance reduction techniques are employed to improve the efficiency of the inference. A simulation study and an application to annual maximum temperatures show that our methods are very effective in finite samples. © 2007 American Statistical Association.
Type
journal article