Delta method in large deviations and moderate deviations for estimators

Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/4454

Title:	Delta method in large deviations and moderate deviations for estimators
Authors:	Gao, F Zhao, X
Issue Date:	Apr-2011
Source:	Annals of statistics, Apr. 2011, v. 39, no. 2, p. 1211-1240
Abstract:	The delta method is a popular and elementary tool for deriving limiting distributions of transformed statistics, while applications of asymptotic distributions do not allow one to obtain desirable accuracy of approximation for tail probabilities. The large and moderate deviation theory can achieve this goal. Motivated by the delta method in weak convergence, a general delta method in large deviations is proposed. The new method can be widely applied to driving the moderate deviations of estimators and is illustrated by examples including the Wilcoxon statistic, the Kaplan–Meier estimator, the empirical quantile processes and the empirical copula function. We also improve the existing moderate deviations results for M-estimators and L-statistics by the new method. Some applications of moderate deviations to statistical hypothesis testing are provided.
Keywords:	Delta method Hypothesis testing Kaplan–Meier estimator Large deviations L-statistics M-estimator Moderate deviations
Publisher:	Institute of Mathematical Statistics
Journal:	Annals of statistics
ISSN:	0003-4851
DOI:	10.1214/10-AOS865
Rights:	© Institute of Mathematical Statistics, 2011. The journal web site is located at http://imstat.org/aos/
Appears in Collections:	Journal/Magazine Article

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