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Saddlepoint approximation for Student’s t-statistic with no moment conditions (2004)

Abstract
A saddlepoint approximation of the Student’s t-statistic was derived by Daniels and Young [Biometrika 78 (1991) 169–179] under the very stringent exponential moment condition that requires that the underlying density function go down at least as fast as a Normal density in the tails. This is a severe restriction on the approximation’s applicability. In this paper we show that this strong exponential moment restriction can be completely dispensed with, that is, saddlepoint approximation of the Student’s t-statistic remains valid without any moment condition. This confirms the folklore that the Student’s t-statistic is robust against outliers. The saddlepoint approximation not only provides a very accurate approximation for the Student’s t-statistic, but it also can be applied much more widely in statistical inference. As a result, saddlepoint approximations should always be used whenever possible. Some numerical work will be given to illustrate these points.

Publication details
Download http://ProjectEuclid.org/getRecord?id=euclid.aos/1107794883
Publisher The Institute of Mathematical Statistics
Repository Project Euclid (Hosted at Cornell University Library) (United States)
Keywords 62E20 (MSC2000), 60G50 (MSC2000), Saddlepoint approximation, large deviation, asymptotic normality, Edgeworth expansion, self-normalized sum, Student’s t-statistic, absolute error, relative error
Type text
Language Englisch

Publications citing this publication (1)
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