A new kernel-projective statistical estimator in the Monte Carlo method

Autor:	Natalya V. Tracheva, Gennady A. Mikhailov, Sergey A. Ukhinov
Rok vydání:	2020
Předmět:	Numerical Analysis Modeling and Simulation Kernel (statistics) Monte Carlo method Applied mathematics Estimator Projective test Mathematics
Zdroj:	Russian Journal of Numerical Analysis and Mathematical Modelling. 35:341-353
ISSN:	1569-3988 0927-6467
DOI:	10.1515/rnam-2020-0028
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::87c59ae802b94e69e07e44acd17e40db https://doi.org/10.1515/rnam-2020-0028 Zobrazit plný text záznamu