A Bootstrap's Reliability Measure on Tests of Hypotheses
Authors: Al Jefferson J. Pabelic, Dennis A. Tarepe
Abstract:
Bootstrapping has gained popularity in different tests of hypotheses as an alternative in using asymptotic distribution if one is not sure of the distribution of the test statistic under a null hypothesis. This method, in general, has two variants – the parametric and the nonparametric approaches. However, issues on reliability of this method always arise in many applications. This paper addresses the issue on reliability by establishing a reliability measure in terms of quantiles with respect to asymptotic distribution, when this is approximately correct. The test of hypotheses used is Ftest. The simulated results show that using nonparametric bootstrapping in F-test gives better reliability than parametric bootstrapping with relatively higher degrees of freedom.
Keywords: F-test, nonparametric bootstrapping, parametric bootstrapping, reliability measure, tests of hypotheses.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1070511
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1699References:
[1] S. Amiri, D. von Rosen, and S. Zwanzig, "On the comparison of parametric and nonparametric bootstrap," Uppsala University Department of Mathematics Report 2008:15. Uppsala University, Uppsala, Sweden, 2008, unpublished.
[2] R. Barlow and F. Proschan, Mathematical Theory of Reliability, SIAM Classics edition, 1996, pp. 5-6.
[3] A. Davison and D. Hinkley, Bootstrap Methods and their Applications. Cambridge, United Kingdom: Cambridge University Press, 1997, ch. 1.
[4] P. Good and J. Hardin, Common Errors in Statistics: How to Avoid Them. New Jersey: John Wiley & Sons, 2005.
[5] J. MacKinnon, "Bootstrap hypothesis testing," Queen-s Economics Department Working Paper No. 1127, Queen-s University, Ontario, Canada, 2007, unpublished.
[6] J. MacKinnon and R. Davidson, "Improving the reliability of bootstrap tests with the fast double bootstrap," Queen-s Economics Department Working Paper No. 1044. Queen-s University, Ontario, Canada, 2006, unpublished.
[7] B. Efron and R. Tibshirani, An Introduction to the Bootstrap, New York: Chapman & Hall, 1993, pp. 31-32.
[8] D. Politis, "The impact of bootstrap methods in time series," Statistical Science, Vol. 18, No. 2, 2003, pp. 219-230.