Abstract
In chapter II, we discussed the choices of the weights, when one wants to bootstrap regular functionals as described in the first chapter. However one can find functionals which do not satisfy the assumptions of chapter I or a sample which is not i.i.d. but is obtained from i.i.d. r.v.’s. The aim of this chapter is to investigate three of such situations : what can we do if we want to bootstrap an empirical process when the parameters are estimated ? How can the extreme values be bootstrapped ? What happens to the bootstrap of the mean when the limiting distribution is non gaussian ?
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© 1995 Springer-Verlag New York, Inc.
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Barbe, P., Bertail, P. (1995). Special Forms of the Bootstrap. In: The Weighted Bootstrap. Lecture Notes in Statistics, vol 98. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2532-4_4
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DOI: https://doi.org/10.1007/978-1-4612-2532-4_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94478-4
Online ISBN: 978-1-4612-2532-4
eBook Packages: Springer Book Archive