HURST EXPONENT ESTIMATES ON SMALL SAMPLES: THE SIMPLEST VERSION OF FEDER'S NON-LINEAR METHOD ERROR COMPENSATOR FOR MODELING ECONOMIC AND BIOMETRIC DATA
Background. Currently, the Hurst exponent is quite easily interpreted in relation to biometric, medical and economic data, but it is customary to evaluate it on large samples. The aim of the work is to eliminate the methodological error that occurs due to small samples of real data. Materials and me...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Penza State University Publishing House
2023-10-01
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| Series: | Надежность и качество сложных систем |
| Subjects: |
| _version_ | 1797657793566605312 |
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| author | Aleksandr I. Ivanov Dmitriy V. Tarasov Kirill A. Gorbunov |
| author_facet | Aleksandr I. Ivanov Dmitriy V. Tarasov Kirill A. Gorbunov |
| author_sort | Aleksandr I. Ivanov |
| collection | DOAJ |
| description | Background. Currently, the Hurst exponent is quite easily interpreted in relation to biometric, medical and economic data, but it is customary to evaluate it on large samples. The aim of the work is to eliminate the methodological error that occurs due to small samples of real data. Materials and methods. The simulation of two-dimensional Brownian motion is used, which gives rise to the possibility of calculating the Hurst exponents. It is proposed by means of simulation modeling to build in advance a nonlinear corrector of methodological errors discovered earlier by E. Feder. Results and conclusions. A relation has been obtained for the value of methodological errors in estimating the Hurst exponent, which makes it possible to correct estimates for small values of the exponent H<0.35 and large values of the exponent H > 0.65. The need to correct methodological errors is growing as the size of small samples of real economic and biometric data decreases. |
| first_indexed | 2024-03-11T17:49:51Z |
| format | Article |
| id | doaj.art-398498a20c3c48f5aa7340978d08fdd3 |
| institution | Directory Open Access Journal |
| issn | 2307-4205 |
| language | English |
| last_indexed | 2024-03-11T17:49:51Z |
| publishDate | 2023-10-01 |
| publisher | Penza State University Publishing House |
| record_format | Article |
| series | Надежность и качество сложных систем |
| spelling | doaj.art-398498a20c3c48f5aa7340978d08fdd32023-10-18T06:42:47ZengPenza State University Publishing HouseНадежность и качество сложных систем2307-42052023-10-013doi:10.21685/2307-4205-2023-3-6HURST EXPONENT ESTIMATES ON SMALL SAMPLES: THE SIMPLEST VERSION OF FEDER'S NON-LINEAR METHOD ERROR COMPENSATOR FOR MODELING ECONOMIC AND BIOMETRIC DATA Aleksandr I. Ivanov0Dmitriy V. Tarasov1Kirill A. Gorbunov2Penza Research Electrotechnical InstitutePenza State UniversityPenza State UniversityBackground. Currently, the Hurst exponent is quite easily interpreted in relation to biometric, medical and economic data, but it is customary to evaluate it on large samples. The aim of the work is to eliminate the methodological error that occurs due to small samples of real data. Materials and methods. The simulation of two-dimensional Brownian motion is used, which gives rise to the possibility of calculating the Hurst exponents. It is proposed by means of simulation modeling to build in advance a nonlinear corrector of methodological errors discovered earlier by E. Feder. Results and conclusions. A relation has been obtained for the value of methodological errors in estimating the Hurst exponent, which makes it possible to correct estimates for small values of the exponent H<0.35 and large values of the exponent H > 0.65. The need to correct methodological errors is growing as the size of small samples of real economic and biometric data decreases. autocorrelation functionalhurst exponentsmall samplesbiometric datamethodological errorerror corrector |
| spellingShingle | Aleksandr I. Ivanov Dmitriy V. Tarasov Kirill A. Gorbunov HURST EXPONENT ESTIMATES ON SMALL SAMPLES: THE SIMPLEST VERSION OF FEDER'S NON-LINEAR METHOD ERROR COMPENSATOR FOR MODELING ECONOMIC AND BIOMETRIC DATA Надежность и качество сложных систем autocorrelation functional hurst exponent small samples biometric data methodological error error corrector |
| title | HURST EXPONENT ESTIMATES ON SMALL SAMPLES: THE SIMPLEST VERSION OF FEDER'S NON-LINEAR METHOD ERROR COMPENSATOR FOR MODELING ECONOMIC AND BIOMETRIC DATA |
| title_full | HURST EXPONENT ESTIMATES ON SMALL SAMPLES: THE SIMPLEST VERSION OF FEDER'S NON-LINEAR METHOD ERROR COMPENSATOR FOR MODELING ECONOMIC AND BIOMETRIC DATA |
| title_fullStr | HURST EXPONENT ESTIMATES ON SMALL SAMPLES: THE SIMPLEST VERSION OF FEDER'S NON-LINEAR METHOD ERROR COMPENSATOR FOR MODELING ECONOMIC AND BIOMETRIC DATA |
| title_full_unstemmed | HURST EXPONENT ESTIMATES ON SMALL SAMPLES: THE SIMPLEST VERSION OF FEDER'S NON-LINEAR METHOD ERROR COMPENSATOR FOR MODELING ECONOMIC AND BIOMETRIC DATA |
| title_short | HURST EXPONENT ESTIMATES ON SMALL SAMPLES: THE SIMPLEST VERSION OF FEDER'S NON-LINEAR METHOD ERROR COMPENSATOR FOR MODELING ECONOMIC AND BIOMETRIC DATA |
| title_sort | hurst exponent estimates on small samples the simplest version of feder s non linear method error compensator for modeling economic and biometric data |
| topic | autocorrelation functional hurst exponent small samples biometric data methodological error error corrector |
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