Abstract
Various tests are available to compare the means of two populations. Tests for skewed data, however, are not well studied even though they are often needed in pharmaceutical study and agricultural economics. In particular, there is no available result to give power and sample size calculation for a two-sample Bootstrap-t test in skewed populations. In this paper, we propose easy-to-compute new tests and study their theoretical properties. The proposed work starts with derivation of a second-order Edgeworth expansion for the pooled two-sample t-statistic. Then new test rejection regions are formed based on Cornish–Fisher expansion of quantiles. The new tests account for first-order and second-order population skewnesses that were ignored in two-sample t test. We report the theoretical type I error accuracy and power of the newly proposed tests and the large sample t test. We also provide the detailed conditions under which the proposed tests give better power than the two-sample large sample test. Our new tests, \(\hbox {TCF}_1\) and TCF, are asymptotically equivalent to Bootstrap-t test up to \(O(N^{-1})\) and \(O(N^{-3/2})\), respectively. Compared with commonly used two-sample parametric and nonparametric tests, the new tests are computationally efficient, give better power for skewed data with moderate sample size, and provide sample size calculation to achieve desired power at a given significance level. Empirical studies confirmed our theoretical results.
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References
An L, Ahmed ES (2008) Improving the performance of kurtosis estimator. Comput Stat Data Anal 52:2669–2681
Barndorff-Nielsen O, Hall P (1988) On the level-error after bartlett adjustment of the likelihood ratio statistic. Biometrika 75:374–378
Beran R (1988) Prepivoting test statistics: A bootstrap view of asymptotic refinements. Journal of the American Statistical Association 83:682–697
Davison AC, Hinkley DV (1997) Bootstrap methods and their application. Cambridge University Press, Cambridge
Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7:1–26
Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman and Hall, New York
Fisher NI, Hall P (1990) On bootstrap hypothesis testing. Aust J Stat 32:177–190
Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531–537
Hall P (1992) The bootstrap and Edgeworth expansion. Springer, New York
Hinkley D (1988) Bootstrap methods. J R Stat Soc B 50:321–337
Ott RL, Longnecker MT (2008) An introduction to statistical methods and data analysis. Duxbury Press, Michigan
Phillip D, Zhou XH (2006) Nonparametric statistical methods for cost-effectiveness analyses. Biometrics 62:576–588
Shao J, Tu D (1995) The jackknife and bootstrap. Springer, New York, NY
Tibshirani R, Hastie T, Narasimhan B, Chu G (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 99:6567–6572
Xu J (2010) Asymptotic expansion of the non null distribution of the two-sample t-statistic under non normality with application in power comparison. Commun Statist Theory Methods 39:1915–1921
Xu J, Cui X, Gupta AK (2009) Improved statistics for contrasting means of two samples under non-normality. Br J Math Stat Psychol 62:21–40
Zhou XH, Philip D (2005) Nonparametric confidence intervals for the one- and two-sample problems. Biostatistics 6:187–200
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The authors would like to thank the two anonymous referees whose comments have led to significant improvement of the paper.
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Bo Tong and Huaiyu Zhang have contributed equally to this paper.
This work was partially supported by a grant by Simons foundation (#246077) to Haiyan Wang.
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Wang, H., Tong, B., Zhang, H. et al. New two-sample tests for skewed populations and their connection to theoretical power of Bootstrap-t test. TEST 26, 661–683 (2017). https://doi.org/10.1007/s11749-017-0530-x
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DOI: https://doi.org/10.1007/s11749-017-0530-x