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Part of the book series: DMV Seminar ((OWS,volume 9))

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Abstract

Assume that Tn = Tn1,...,ξn,F) is a function of an i.i.d. sample and its underlying d.f.F. The importance of statistical large sample theory comes from the fact that the law ℒ(Tn) of Tn in many situations is not available, but may well be approximated by some normal distribution, say.

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References (and further reading)

  • Bickel, P.J. and Freedman, D.A. (1981). Some asymptotic theory for the bootstrap. Ann. Statist. 9, 1196–1217.

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  • Efron, B. (1979). Bootstrap methods: another look at the jackknife. Ann. Statist. 7, 1–26.

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  • Efron, B. (1982). The jackknife, the bootstrap, and other resampling schemes. Philadelphia: SIAM, Reg. Conf. Ser. 38.

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  • Freedaan, D.A. (1981). Bootstrapping regression models. Ann. Statist. 9, 1218–1228.

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  • Ghosh, M., Parr, W., Singh, K. and Babu, J. (1984). A note on bootstrapping the sample median. Ann. Statist. 12, 1130–1135.

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  • Klenk, A. and Stute, W. (1987). Bootstrapping of Lrestimates. Statist, and Decis. 5, 77–87.

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  • Singh, K. (1981). On the asymptotic accuracy of Efron’s bootstrap. Ann. Statist. 9, 1187–1195.

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© 1987 Springer Basel AG

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Gaenssler, P., Stute, W. (1987). Bootstrapping. In: Seminar on Empirical Processes. DMV Seminar, vol 9. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-6269-1_8

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  • DOI: https://doi.org/10.1007/978-3-0348-6269-1_8

  • Publisher Name: Birkhäuser, Basel

  • Print ISBN: 978-3-7643-1921-2

  • Online ISBN: 978-3-0348-6269-1

  • eBook Packages: Springer Book Archive

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