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Power, FDR and conservativeness of BB-SGoF method

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  • Irene Castro-Conde
  • Jacobo Uña-Álvarez

Abstract

Beta-binomial sequential goodness-of-fit (or BB-SGoF) method for multiple testing has been recently proposed as a suitable modification of the sequential goodness-of-fit (SGoF) multiple testing method when the tests are correlated in blocks. In this paper we investigate for the first time the power, the FDR and the conservativeness of BB-SGoF in an intensive Monte Carlo simulation study. Important features such as automatic selection of the number of existing blocks and preliminary testing for independence are explored. Our study reveals that (a) BB-SGoF method roughly maintains the properties of original SGoF in the dependent case, reporting a small value for the probability that the number of false positives exceeds the number of false negatives with p value below $$\gamma $$ γ ; (b) BB-SGoF weakly controls for FDR even when the beta-binomial model is violated and the number of blocks $$k$$ k is unknown; and that (c) the loss of power of the automatic selector for the number of blocks relative to the benchmark method which uses the true $$k$$ k varies depending on the proportion and the type (strong, intermediate or weak) of the effects, being strongly influenced by the within-block correlation too. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Irene Castro-Conde & Jacobo Uña-Álvarez, 2015. "Power, FDR and conservativeness of BB-SGoF method," Computational Statistics, Springer, vol. 30(4), pages 1143-1161, December.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:4:p:1143-1161
    DOI: 10.1007/s00180-015-0553-2
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    References listed on IDEAS

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    1. van der Laan Mark J. & Dudoit Sandrine & Pollard Katherine S., 2004. "Augmentation Procedures for Control of the Generalized Family-Wise Error Rate and Tail Probabilities for the Proportion of False Positives," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-27, June.
    2. Mark van der Laan & Sandrine Dudoit & Katherine Pollard, 2004. "Multiple Testing. Part III. Procedures for Control of the Generalized Family-Wise Error Rate and Proportion of False Positives," U.C. Berkeley Division of Biostatistics Working Paper Series 1140, Berkeley Electronic Press.
    3. B. Moerkerke & E. Goetghebeur & J. De Riek & I. Roldán‐Ruiz, 2006. "Significance and impotence: towards a balanced view of the null and the alternative hypotheses in marker selection for plant breeding," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 61-79, January.
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    Cited by:

    1. P. Givord & M. Suarez Castillo, 2019. "Excellence for all? Heterogeneity in high-schools’ value-added," Documents de Travail de l'Insee - INSEE Working Papers g2019-14, Institut National de la Statistique et des Etudes Economiques.
    2. repec:hal:spmain:info:hdl:2441/1dniduq06u8se8q5enfvnorti9 is not listed on IDEAS
    3. repec:hal:journl:hal-03389176 is not listed on IDEAS
    4. repec:spo:wpmain:info:hdl:2441/1dniduq06u8se8q5enfvnorti9 is not listed on IDEAS

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