New Statistical Kernel-Projection Estimator in the Monte Carlo Method

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New Statistical Kernel-Projection Estimator in the Monte Carlo Method. / Mikhailov, G. A.; Tracheva, N. V.; Ukhinov, S. A.

In: Doklady Mathematics, Vol. 102, No. 1, 01.07.2020, p. 313-317.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{f326dd1583bd40bbbb7baefed6676359,

title = "New Statistical Kernel-Projection Estimator in the Monte Carlo Method",

abstract = "The statistical kernel estimator in the Monte Carlo method is usually optimized based on the preliminary construction of a {"}microgrouped{"} sample of values of the variable under study. Even for the two-dimensional case, such optimization is very difficult. Accordingly, we propose a combined (kernel-projection) statistical estimator of the two-dimensional distribution density: a kernel estimator is constructed for the first (main) variable, and a projection estimator, for the second variable. In this case, for each kernel interval determined by the microgrouped sample, the coefficients of a particular orthogonal decomposition of the conditional probability density are statistically estimated based on preliminary results for the {"}micro intervals.{"} An important result of this work is the mean-square optimization of such an estimator under assumptions made about the convergence rate of the orthogonal decomposition in use. The constructed estimator is verified by evaluating the bidirectional distribution of a radiation flux passing through a layer of scattering and absorbing substance.",

keywords = "kernel density estimator, kernel-projection estimator, Monte Carlo method, projection estimator, ANGULAR-DISTRIBUTIONS",

author = "Mikhailov, {G. A.} and Tracheva, {N. V.} and Ukhinov, {S. A.}",

note = "Funding Information: The computations were carried out using resources of the Shared Facility Center at the Siberian Supercomputer Center of the Institute of Computational Mathematics and Mathematical Geophysics of the Siberian Branch of the Russian Academy of Science [10]. Funding Information: This work was supported by the Russian Foundation for Basic Research, project no. 18-01-00356. Publisher Copyright: {\textcopyright} 2020, Pleiades Publishing, Ltd. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "2020",

month = jul,

day = "1",

doi = "10.1134/S1064562420040122",

language = "English",

volume = "102",

pages = "313--317",

journal = "Doklady Mathematics",

issn = "1064-5624",

publisher = "Maik Nauka-Interperiodica Publishing",

number = "1",

}

RIS

TY - JOUR

T1 - New Statistical Kernel-Projection Estimator in the Monte Carlo Method

AU - Mikhailov, G. A.

AU - Tracheva, N. V.

AU - Ukhinov, S. A.

N1 - Funding Information: The computations were carried out using resources of the Shared Facility Center at the Siberian Supercomputer Center of the Institute of Computational Mathematics and Mathematical Geophysics of the Siberian Branch of the Russian Academy of Science [10]. Funding Information: This work was supported by the Russian Foundation for Basic Research, project no. 18-01-00356. Publisher Copyright: © 2020, Pleiades Publishing, Ltd. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/7/1

Y1 - 2020/7/1

N2 - The statistical kernel estimator in the Monte Carlo method is usually optimized based on the preliminary construction of a "microgrouped" sample of values of the variable under study. Even for the two-dimensional case, such optimization is very difficult. Accordingly, we propose a combined (kernel-projection) statistical estimator of the two-dimensional distribution density: a kernel estimator is constructed for the first (main) variable, and a projection estimator, for the second variable. In this case, for each kernel interval determined by the microgrouped sample, the coefficients of a particular orthogonal decomposition of the conditional probability density are statistically estimated based on preliminary results for the "micro intervals." An important result of this work is the mean-square optimization of such an estimator under assumptions made about the convergence rate of the orthogonal decomposition in use. The constructed estimator is verified by evaluating the bidirectional distribution of a radiation flux passing through a layer of scattering and absorbing substance.

AB - The statistical kernel estimator in the Monte Carlo method is usually optimized based on the preliminary construction of a "microgrouped" sample of values of the variable under study. Even for the two-dimensional case, such optimization is very difficult. Accordingly, we propose a combined (kernel-projection) statistical estimator of the two-dimensional distribution density: a kernel estimator is constructed for the first (main) variable, and a projection estimator, for the second variable. In this case, for each kernel interval determined by the microgrouped sample, the coefficients of a particular orthogonal decomposition of the conditional probability density are statistically estimated based on preliminary results for the "micro intervals." An important result of this work is the mean-square optimization of such an estimator under assumptions made about the convergence rate of the orthogonal decomposition in use. The constructed estimator is verified by evaluating the bidirectional distribution of a radiation flux passing through a layer of scattering and absorbing substance.

KW - kernel density estimator

KW - kernel-projection estimator

KW - Monte Carlo method

KW - projection estimator

KW - ANGULAR-DISTRIBUTIONS

UR - http://www.scopus.com/inward/record.url?scp=85092911079&partnerID=8YFLogxK

U2 - 10.1134/S1064562420040122

DO - 10.1134/S1064562420040122

M3 - Article

AN - SCOPUS:85092911079

VL - 102

SP - 313

EP - 317

JO - Doklady Mathematics

JF - Doklady Mathematics

SN - 1064-5624

IS - 1

ER -

ID: 25653534