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Variance reduction in multiparameter likelihood models
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DOI：10.1198/016214506000000807.png)
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Abstract
En 中文
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.
Keywords:
bootstrap
extreme value distribution
generalized linear models
local likelihood
local linear MLE
logistic regression
variance reduction
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