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Saddlepoint approximation for student's t-statistic with no moment conditions

  • Bing Yi Jing*
  • , Qi Man Shao
  • , Wang Zhou
  • *Corresponding author for this work

Research output: Contribution to journalJournal Articlepeer-review

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.

Original languageEnglish
Pages (from-to)2679-2711
Number of pages33
JournalAnnals of Statistics
Volume32
Issue number6
DOIs
Publication statusPublished - Dec 2004

Keywords

  • Absolute error
  • Asymptotic normality
  • Edgeworth expansion
  • Large deviation
  • Relative error
  • Saddlepoint approximation
  • Self-normalized sum
  • Student's t-statistic

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