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Asymptotically Efficient Importance Sampling for Bootstrap. / Ermakov, M. S.

в: Journal of Mathematical Sciences (United States), Том 214, № 4, 01.04.2016, стр. 474-483.

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Harvard

Ermakov, MS 2016, 'Asymptotically Efficient Importance Sampling for Bootstrap', Journal of Mathematical Sciences (United States), Том. 214, № 4, стр. 474-483. https://doi.org/10.1007/s10958-016-2791-4

APA

Ermakov, M. S. (2016). Asymptotically Efficient Importance Sampling for Bootstrap. Journal of Mathematical Sciences (United States), 214(4), 474-483. https://doi.org/10.1007/s10958-016-2791-4

Vancouver

Ermakov MS. Asymptotically Efficient Importance Sampling for Bootstrap. Journal of Mathematical Sciences (United States). 2016 Апр. 1;214(4):474-483. https://doi.org/10.1007/s10958-016-2791-4

Author

Ermakov, M. S. / Asymptotically Efficient Importance Sampling for Bootstrap. в: Journal of Mathematical Sciences (United States). 2016 ; Том 214, № 4. стр. 474-483.

BibTeX

@article{88c95b0291724441bd852d0d9e898cbd,
title = "Asymptotically Efficient Importance Sampling for Bootstrap",
abstract = "The Large Deviation Principle is proved for the conditional probabilities of moderate deviations of weighted empirical bootstrap measures with respect to a fixed empirical measure. Using this LDP for the problem of calculation of moderate deviation probabilities of differentiable statistical functionals, it is shown that the importance sampling based on influence function is asymptotically efficient.",
author = "Ermakov, {M. S.}",
note = "Publisher Copyright: {\textcopyright} 2016, Springer Science+Business Media New York. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.",
year = "2016",
month = apr,
day = "1",
doi = "10.1007/s10958-016-2791-4",
language = "English",
volume = "214",
pages = "474--483",
journal = "Journal of Mathematical Sciences",
issn = "1072-3374",
publisher = "Springer Nature",
number = "4",

}

RIS

TY - JOUR

T1 - Asymptotically Efficient Importance Sampling for Bootstrap

AU - Ermakov, M. S.

N1 - Publisher Copyright: © 2016, Springer Science+Business Media New York. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.

PY - 2016/4/1

Y1 - 2016/4/1

N2 - The Large Deviation Principle is proved for the conditional probabilities of moderate deviations of weighted empirical bootstrap measures with respect to a fixed empirical measure. Using this LDP for the problem of calculation of moderate deviation probabilities of differentiable statistical functionals, it is shown that the importance sampling based on influence function is asymptotically efficient.

AB - The Large Deviation Principle is proved for the conditional probabilities of moderate deviations of weighted empirical bootstrap measures with respect to a fixed empirical measure. Using this LDP for the problem of calculation of moderate deviation probabilities of differentiable statistical functionals, it is shown that the importance sampling based on influence function is asymptotically efficient.

UR - http://www.scopus.com/inward/record.url?scp=84961182206&partnerID=8YFLogxK

U2 - 10.1007/s10958-016-2791-4

DO - 10.1007/s10958-016-2791-4

M3 - Article

AN - SCOPUS:84961182206

VL - 214

SP - 474

EP - 483

JO - Journal of Mathematical Sciences

JF - Journal of Mathematical Sciences

SN - 1072-3374

IS - 4

ER -

ID: 71601433