A BINOMIAL MODEL APPROXIMATION FOR MULTIPLE TESTING
DOI:
https://doi.org/10.51406/jnset.v16i2.1850Keywords:
Bonferroni Procedure, False Discovery Rate, Binomial Model Approximation, False Positives, False Negatives, Multiple TestingAbstract
Multiple testing is associated with simultaneous testing of many hypotheses, and frequently calls for adjusting level of significance in some way that the probability of observing at least one significant result due to chance remains below the desired significance levels. This study developed a Binomial Model Approximations (BMA) method as an alternative to addressing the multiplicity problem associated with testing more than one hypothesis at a time. The proposed method has demonstrated capacity for controlling Type I Error Rate as sample size increases when compared with the existing Bonferroni and False Discovery Rate (FDR).
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Westfall., Young. 1993. Resampling-Based Multiple Testing: Examples and Methods for p-value Adjustment. ISBN: 978-0-471-55761-6
Benjamini., Hochberg 1995. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, series B (methodological), Vol.57,NO.1, 289-300
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de Una-Alvarez J. 2015. The Beta-Binonual SGoF method for multiple dependent tests, Statistical Application in Genetics and Molecular Biology, 11(3), 198-106
Dunnett., Tamhane. 1992. Calculation of critical values of Dunnet and Tamhane’s Step-up Multiple Test Procedure. Journal of America Statistical Association, 87, 162-170.
Gaetano J. 2013. Holm-Bonferroni sequential correction: An Excel calculator (1.1) doi:10.13140/RG.2.1.4466.9927
Garcia L.V, 2003. Controlling the false discovery rate in ecological research. Trends in Ecology and Evolution. 18: 553 -554.
Gordon. A., Glazko. G., Qiu. X., Yakovlev. A. 2007. Control of The Mean Number of False Discovery, Bonferroni and Stability of Multiple Testing. Anals of Applied Statistics. Volume 1, Number 1, 179-190, Doi:10.1214/07-A0AS102
Hochberg, Y. 1988. A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75(4): 800-802. https://doi.org/10.093/biomet/7.
Holm. S. A. 1979. A simple sequentially rejective multiple test procedure. Scandinavian Journal of statistics, 6, 65-70.
Hommel, G.A. 1988. A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika. 75: 383-386.
Peter H Westfall., S. Stanley Young. 1989. P-value Adjustment for multiple tests inMultivariate Binomial Models. Journal of the American statistical association 84(407): 780-789.
Savitz DA., Olshan AF. 1995. Multiple Comparison and Related Issues in the Interpretation of Epidemiologic data. Am J Epidemiol. 142: 904-908.
Shinichi Nakagawa. 2004. A farewell to Bonferroni: The problems of low statistical power and publication bias. Behavioural Ecology 15(6): 1044- 1045.
Sidak Z. 1967. Rectangular confidence regions for the means of multivariate normal distribution. Journal of the American Statistical Association. 62(318): 626-633, doi:10.1080/01621459.1967.10482935.
Storey, J.D. 2003. The positive false discovery rate. A Bayesian interpretation and the q-values. The annals of statistics 31(6): 2013-2035.
Westfall., Young. 1993. Resampling-Based Multiple Testing: Examples and Methods for p-value Adjustment. ISBN: 978-0-471-55761-6
