Tail approximations for the Student \(t\)-, \(F\)-, and Welch statistics for non-normal and not necessarily i.i.d. random variables
DOI10.3150/13-BEJ552zbMath1333.62053arXiv1410.6033OpenAlexW2950416934MaRDI QIDQ470067
Publication date: 11 November 2014
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1410.6033
outliersdependent random variablestest power\(F\)-testsmall sample sizehigh-throughput screeningnon-homogeneous datanon-normal population distributionStudent's one- and two-sample \(t\)-statisticssystematic effectsWelch statistics
Asymptotic distribution theory in statistics (62E20) Approximations to statistical distributions (nonasymptotic) (62E17)
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- Tail approximations for the Student \(t\)-, \(F\)-, and Welch statistics for non-normal and not necessarily i.i.d. random variables
- Edgeworth expansion for Student's t statistic under minimal moment conditions
- The positive false discovery rate: A Bayesian interpretation and the \(q\)-value
- Saddlepoint approximation for Student's \(t\)-statistic with no moment conditions
- Tail probability approximations for Student's \(t\)-statistics
- Efficient estimation of the number of false positives in high-throughput screening
- Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rates: A Unified Approach
- A Direct Approach to False Discovery Rates
- THE DISTRIBUTION OF ‘STUDENT'S’ t IN RANDOM SAMPLES OF ANY SIZE DRAWN FROM NON-NORMAL UNIVERSES
- THE DISTRIBUTION OF THE VARIANCE RATIO IN RANDOM SAMPLES OF ANY SIZE DRAWN FROM NON-NORMAL UNIVERSES
- Corrections for Nonnormality in the Use of the Two-Sample $t$- and $F$-Test at High Significance Levels
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