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 language | English |
|---|---|
| Pages (from-to) | 2679-2711 |
| Number of pages | 33 |
| Journal | Annals of Statistics |
| Volume | 32 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 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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