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Controlling the familywise error rate with plug‐in estimator for the proportion of true null hypotheses

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  • Helmut Finner
  • Veronika Gontscharuk

Abstract

Summary. Estimation of the number or proportion of true null hypotheses in multiple‐testing problems has become an interesting area of research. The first important work in this field was performed by Schweder and Spjøtvoll. Among others, they proposed to use plug‐in estimates for the proportion of true null hypotheses in multiple‐test procedures to improve the power. We investigate the problem of controlling the familywise error rate FWER when such estimators are used as plug‐in estimators in single‐step or step‐down multiple‐test procedures. First we investigate the case of independent p‐values under the null hypotheses and show that a suitable choice of plug‐in estimates leads to control of FWER in single‐step procedures. We also investigate the power and study the asymptotic behaviour of the number of false rejections. Although step‐down procedures are more difficult to handle we briefly consider a possible solution to this problem. Anyhow, plug‐in step‐down procedures are not recommended here. For dependent p‐values we derive a condition for asymptotic control of FWER and provide some simulations with respect to FWER and power for various models and hypotheses.

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  • Helmut Finner & Veronika Gontscharuk, 2009. "Controlling the familywise error rate with plug‐in estimator for the proportion of true null hypotheses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 1031-1048, November.
    • Handle: RePEc:bla:jorssb:v:71:y:2009:i:5:p:1031-1048
    DOI: 10.1111/j.1467-9868.2009.00719.x
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    2. Jules Ellis, 2014. "An Inequality for Correlations in Unidimensional Monotone Latent Variable Models for Binary Variables," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 303-316, April.
    3. Marina Bogomolov & Ruth Heller, 2018. "Assessing replicability of findings across two studies of multiple features," Biometrika, Biometrika Trust, vol. 105(3), pages 505-516.
    4. Bryan D. MacGregor & Rainer Schulz & Yuan Zhao, 2021. "Performance and Market Maturity in Mutual Funds: Is Real Estate Different?," The Journal of Real Estate Finance and Economics, Springer, vol. 63(3), pages 437-492, October.
    5. Zhao, Haibing, 2014. "Adaptive FWER control procedure for grouped hypotheses," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 63-70.
    6. Axel Gandy & Georg Hahn, 2016. "A Framework for Monte Carlo based Multiple Testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1046-1063, December.
    7. Haibing Zhao & Wing Kam Fung, 2018. "Controlling mixed directional false discovery rate in multidimensional decisions with applications to microarray studies," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 316-337, June.
    8. Dickhaus Thorsten & Straßburger Klaus & Schunk Daniel & Morcillo-Suarez Carlos & Illig Thomas & Navarro Arcadi, 2012. "How to analyze many contingency tables simultaneously in genetic association studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-33, July.
    9. Anh-Tuan Hoang & Thorsten Dickhaus, 2022. "On the usage of randomized p-values in the Schweder–Spjøtvoll estimator," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 289-319, April.

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