Bootstrapping modified goodness-of-fit statistics with estimated parameters
Goodness-of-fit tests are proposed for testing a composite null hypothesis that is a general parametric family of distribution functions. They are distribution-free under the null hypothesis and have a limiting normal distribution under the null and the alternative hypothesis. To avoid the estimation of the asymptotic variance under the alternative hypothesis, we propose consistent bootstrap estimators.
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Volume (Year): 71 (2005)
Issue (Month): 2 (February)
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References listed on IDEAS
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- Koul, H. L. & Lahiri, S. N., 1994. "On Bootstrapping M-Estimated Residual Processes in Multiple Linear-Regression Models," Journal of Multivariate Analysis, Elsevier, vol. 49(2), pages 255-265, May.
- Too, Yeu-Hua & Lin, Gwo Dong, 1989. "Characterizations of uniform and exponential distributions," Statistics & Probability Letters, Elsevier, vol. 7(5), pages 357-359, April.
- Jiménez-Gamero, M. D. & Muñoz-García, J. & Pino-Mejías, R., 2003. "Bootstrapping parameter estimated degenerate U and V statistics," Statistics & Probability Letters, Elsevier, vol. 61(1), pages 61-70, January.
- Ibrahim A. Ahmad & Ibrahim A. Alwasel, 1999. "A Goodness-of-fit Test for Exponentiality Based on the Memoryless Property," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 681-689.
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