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An efficient and flexible multiplicity adjustment for chi-square endpoints. (English) Zbl 07610872

MSC:

62-XX Statistics

References:

[1] R Core Team, R: A language and environment for statistical computing, R Found Stat. Comput., Vienna, Austria, 2019. http://www.R-project.org/.
[2] K. S. Pollard, S. Dudoit, M. J. {van der Laan}, Multiple testing procedures: R multtest package and applications to Genomics, Bioinformatics and Computational Biology Solutions Using R and Bioconductor, Springer, 2005.
[3] T, Simultaneous inference in general parametric models, Biomet. J., 50, 346-363 (2008) · Zbl 1442.62415 · doi:10.1002/bimj.200810425
[4] C, Brunner, A new user specific multiple testing method for business applications: The SiMaFlex procedure, J. Stat. Plan Infer., Online (2021) · Zbl 1465.62179
[5] Y, Controlling the false discovery rate: A practical and powerful approach to multiple testing, J. Royal Stat. Soc. B., 57, 289-300 (1995) · Zbl 0809.62014
[6] Y, The control of the false discovery rate in multiple testing under dependency, Ann. Stat., 29, 1165-1188 (2001) · Zbl 1041.62061
[7] D, An upper bound for the probability of a union, J. Appl. Probab., 13, 597-603 (1976) · Zbl 0349.60007 · doi:10.2307/3212481
[8] M, A path length inequality for the multivariate-t distribution, with applications to multiple comparisons, J. Am. Stat. Assoc., 91, 211-216 (1996) · Zbl 0870.62041
[9] D, Simultaneous confidence bounds in multiple regression using predictor variable constraints, J. Am. Stat. Assoc., 82, 214-219 (1987) · Zbl 0617.62036 · doi:10.1080/01621459.1987.10478422
[10] J. Sun, C. R. Loader, Simultaneous confidence bands for linear regression and smoothing, Ann. Stat., (1994), 1328-1345. · Zbl 0817.62057
[11] K, An improved Bonferroni inequality and applications, Biometrika, 69, 297-302 (1982) · Zbl 0497.62027 · doi:10.1093/biomet/69.2.297
[12] M, Comparison of statistical methods for analysis of clustered binary observations, Stat. Med., 24, 911-923 (2005) · doi:10.1002/sim.1958
[13] R, A power study of a sequential method of p-value adjustment for correlated continuous endpoints, J. Biopharm. Stat., 8, 585-598 (1998) · Zbl 1079.62550 · doi:10.1080/10543409808835262
[14] S, The approximate multinormal probabilities applied to correlated multiple endpoints in clinical trials, Stat. Med., 10, 1123-1135 (1991) · doi:10.1002/sim.4780100712
[15] S. J. Pocock, N. L. Geller, A. A. Tsiatis, The analysis of multiple endpoints in clinical trials, Biometrics, (1987), 487-498.
[16] P. C. O’Brien, Procedures for comparing samples with multiple endpoints, Biometrics, (1984), 1079-1087.
[17] R. E. Tarone, A modified Bonferroni method for discrete data, Biometrics, (1990), 515-522. · Zbl 0715.62140
[18] P, Multiple testing of general contrasts, J. Am. Stat. Assoc., 92, 299-306 (2007)
[19] P, Multiple testing with minimal assumptions, Biomet. J., 50, 745-755 (2008) · Zbl 1442.62690 · doi:10.1002/bimj.200710456
[20] W, Asymptotic tests of association with multiple SNPs in linkage disequilibrium, Genet. Epidemiol., 33, 497-507 (2009) · doi:10.1002/gepi.20402
[21] J, Computing and approximating multivariate chi-square probabilities, J. Stat. Comput. Sim., 86, 1233-1247 (2016) · Zbl 1510.62075 · doi:10.1080/00949655.2015.1058798
[22] S. Dudoit, M. J. van der Laan, Multiple tests of association with biological annotation metadata, in Multiple Testing Procedures with Applications to Genomics, Springer Series in Statistics, Springer, (2008), 413-476. · Zbl 1261.62014
[23] K, Self-validated Computations for the Probabilities of the Central Bivariate Chi-square Distribution and a Bivariate F Distribution, J. Stat. Comput. Sim., 72, 63-75 (2002) · Zbl 1091.62502 · doi:10.1080/00949650211422
[24] P. R. Krishnaiah (Ed.), Handbook of statistics, Motilal Banarsidass Publisher, (1980).
[25] J, On the shortest spanning subtree of a graph and the traveling salesman problem, Proc. Am. Math. Soc., 7, 48-50 (1956) · Zbl 0070.18404 · doi:10.1090/S0002-9939-1956-0078686-7
[26] A, Neyman’s C() and Silvey’s LM tests: An essay on historical developments and some new results, J. Stat. Plan Infer., 97, 9-44 (2001) · Zbl 1053.62023 · doi:10.1016/S0378-3758(00)00343-8
[27] F, Learning the optimal scale for GWAS through hierarchical SNP aggregation, BMC Bioinform., 19, 1-14 (2018) · doi:10.1186/s12859-017-2006-0
[28] G, On Rao score and Pearson \(\chi^2\) statistics in generalized linear models, Stat. Papers, 46, 555-574 (2005) · Zbl 1102.62077 · doi:10.1007/BF02763005
[29] D. Pregibon, Score tests in GLIM with applications, In GLIM82: Proceedings of the International Conference on Generalized Linear Models, R Gilchrist (ed.), Lec. Notes Stat., 14, Springer, New York, (1982), 87-97. · Zbl 0493.62063
[30] G. K. Smyth, Pearson’s goodness of fit statistic as a score test statistic, Science and Statistics: A Festschrift for Terry Speed, D. R. Goldstein (ed.), IMS Lec Notes., 40, Institute of Mathematical Statistics, Beachwood, Ohio, (2003), 115-126. http://www.statsci.org/smyth/pubs/goodness.pdf. · Zbl 1029.00084
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