Systems and Means of Informatics
2026, Volume 36, Issue 2, pp 40-61
STATISTICAL ANALYSIS OF THE EQUIVALENCE OF ALGORITHMS FOR ESTIMATING PARAMETERS OF DYNAMICAL-STOCHASTIC MODELS OF TURBULENT HEAT EXCHANGE BETWEEN THE OCEAN AND ATMOSPHERE
Abstract
The paper presents a statistical analysis of the equivalence between nonparametric and semiparametric approaches to estimating the random coefficients of an Ito stochastic differential equation which is employed to model turbulent heat fluxes between the ocean and atmosphere. The Wilcoxon-Mann- Whitney test was utilized to evaluate the performance of these estimators as a function of the number of bins used during the input data discretization phase.
The study was conducted using both synthetic data sets with predefined parameters and empirical ERA5 reanalysis data for the North Atlantic. The results indicate that as the number of bins increases, the accuracy of the nonparametric method deteriorates for the drift coefficient but improves for the diffusion coefficient, whereas the semiparametric method demonstrates high stability. Based on statistical testing with the Holm-Bonferroni correction for multiple hypotheses testing, a threshold number of bins (approximately 200-250) was identified, beyond which the distributions of estimates from both methods become statistically indistinguishable. This confirms their practical equivalence for the analysis of geophysical data.
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[+] About this article
Title
STATISTICAL ANALYSIS OF THE EQUIVALENCE OF ALGORITHMS FOR ESTIMATING PARAMETERS OF DYNAMICAL-STOCHASTIC MODELS OF TURBULENT HEAT EXCHANGE BETWEEN THE OCEAN AND ATMOSPHERE
Journal
Systems and Means of Informatics
Volume 36, Issue 2, pp 40-61
Cover Date
2026-06-05
DOI
10.14357/08696527260203
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
Ito stochastic differential equation; random coefficients; EM algorithm
Authors
A. A. Osipova
Author Affiliations
 Federal Research Center "Computer Science and Control", Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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