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From |
"Airey, David C" <david.airey@vanderbilt.edu> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: RE: RE: two sample test under generalized Behrens-Fisher conditions |

Date |
Tue, 14 Dec 2010 15:46:37 -0600 |

. > I don't think that highly of t-tests. To quote Hampel et al. (Robust Statistics: The Approach Based on Influence Functions, Wiley, NY, 1986) p. 405: > "Many statisticians are proud of the so-called robustness of the t- test and more generally of the test in fixed-effects models in the analysis of variance. But this robustness is only a rather moderate and limited robustness of level ("robustness of validity"); the power ("robustness of efficiencey") and hence also the length of confidenceintervals and the size of standard erros is very nonrobust. Consequently, a significant result can be believed, but non- significance may just be due to the inefficiency of least squares." > Perhaps the easiest alternative to teach would be one based on trimmed means, which are not only easy to understand (as opposed to, say, M- Estimators and robust regression), but, unlike the median, have an easy standard error formula. > Steve > sjsamuels@gmail.com Thanks for pointing that out. A nice blurb about the trimmed mean t-test here: <http://mres.gmu.edu/pmwiki/uploads/Main/Ng2008.pdf>. I guess the seminal article on this was: Yuen, K.K. (1974). The two-sample trimmed t-test for unequal population variances. Biometrika, 61, 165-170. But also interesting is a paper by Keselman et al.: Keselman HJ, Kowalchuk RK, Lix LM (1998) Robust nonorthogonal analses revisited: an update base on trimmed means. Psychometrika 63:1 145-163 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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