Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
A new kernel-projective statistical estimator in the Monte Carlo method. / Mikhailov, Gennady A.; Tracheva, Natalya V.; Ukhinov, Sergey A.
в: Russian Journal of Numerical Analysis and Mathematical Modelling, Том 35, № 6, 01.12.2020, стр. 341-353.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - A new kernel-projective statistical estimator in the Monte Carlo method
AU - Mikhailov, Gennady A.
AU - Tracheva, Natalya V.
AU - Ukhinov, Sergey A.
N1 - Publisher Copyright: © 2020 Walter de Gruyter GmbH, Berlin/Boston 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - In the present paper, we propose a new combined kernel-projective statistical estimator of the two-dimensional distribution density, where the first 'main' variable is processed with the kernel estimator, and the second one is processed with the projective estimator for the conditional distribution density. In this case, statistically estimated coefficients of some orthogonal expansion of the conditional distribution density are used for each 'kernel' interval defined by a micro-sample. The root-mean-square optimization of such an estimator is performed under the assumptions concerning the convergence rate of the used orthogonal expansion. The numerical study of the constructed estimator is implemented for angular distributions of the radiation flux forward-scattered and backscattered by a layer of matter. A comparative analysis of the results is performed for molecular and aerosol scattering.
AB - In the present paper, we propose a new combined kernel-projective statistical estimator of the two-dimensional distribution density, where the first 'main' variable is processed with the kernel estimator, and the second one is processed with the projective estimator for the conditional distribution density. In this case, statistically estimated coefficients of some orthogonal expansion of the conditional distribution density are used for each 'kernel' interval defined by a micro-sample. The root-mean-square optimization of such an estimator is performed under the assumptions concerning the convergence rate of the used orthogonal expansion. The numerical study of the constructed estimator is implemented for angular distributions of the radiation flux forward-scattered and backscattered by a layer of matter. A comparative analysis of the results is performed for molecular and aerosol scattering.
KW - kernel statistical estimator
KW - kernel-projective estimator
KW - Monte Carlo method
KW - orthogonal expansion
KW - projective estimator
KW - radiation transfer
UR - http://www.scopus.com/inward/record.url?scp=85098186020&partnerID=8YFLogxK
U2 - 10.1515/rnam-2020-0028
DO - 10.1515/rnam-2020-0028
M3 - Article
AN - SCOPUS:85098186020
VL - 35
SP - 341
EP - 353
JO - Russian Journal of Numerical Analysis and Mathematical Modelling
JF - Russian Journal of Numerical Analysis and Mathematical Modelling
SN - 0927-6467
IS - 6
ER -
ID: 27297411