Comparative analysis of methods for calculating the mode of a stationary random process

Vladimir Marchuk,Ivan Nikishin,Igor Shrayfel,Gleb Hripkov,A Lukyanov,P.D Tung,V Mladenovic,S Antipov,E.H Abderrazzak,M Petkovic

doi:10.1051/e3sconf/202127902004

Vladimir Marchuk, Ivan Nikishin + Show 8 more

Open Access

https://doi.org/10.1051/e3sconf/202127902004

Copy DOI

Abstract

The paper considers methods for calculating the mode of a stationary random process when solving the problem of processing measurement results under conditions of a priori uncertainty. The results of the conducted studies allowed us to conclude that the most effective method for calculating the mode for a given sample is the proposed method, which allows to increase the accuracy of its calculation by at least 8 times, compared to methods based on the construction of histograms. It should be noted that the proposed method allows to provide an estimate with an error of at least 5% for samples with a number of measurements of about 5 value.

Highlights

IntroductionWhen processing random stationary processes, in some cases, it is necessary to calculate the mode of a random process
When processing random stationary processes, in some cases, it is necessary to calculate the mode of a random process. One example of such a need is the use of the method of multiplying estimates (RAZOTS) when processing measurement results under conditions of a priori uncertainty [1-3]
As follows from the analysis of the research results presented in the paper [4, 5], the distribution of the error in each of the cross sections of the multiplication set of the original sample is assumed according to the normal law

Summary

Introduction

When processing random stationary processes, in some cases, it is necessary to calculate the mode of a random process One example of such a need is the use of the method of multiplying estimates (RAZOTS) when processing measurement results under conditions of a priori uncertainty [1-3]. It is known that under the Rayleigh distribution law, the values of the mode and the mean are already significantly different compared to the normal law, where they are equal. This difference is the source of the increase in the error of processing the measurement results, which can be reduced by calculating not the average, but the mode in each of the cross-sections of processing the measurement results. Keeping in mind that a mode is understood as the value of a random variable, the probability of which is maximal

Methods

Results

Conclusion