https://oldena.lpnu.ua/handle/ntb/52502| Title: | Robust Approach to Estimation of the Intensity of Noisy Signal with Additive Uncorrelated Impulse Interference |
| Authors: | Lozynskyy, Andriy Romanyshyn, Igor Rusyn, Bohdan Minialo, Volodymyr |
| Affiliation: | Karpenko Phisico-Mechanical Institute NAS of Ukraine |
| Bibliographic description (Ukraine): | Robust Approach to Estimation of the Intensity of Noisy Signal with Additive Uncorrelated Impulse Interference / Andriy Lozynskyy, Igor Romanyshyn, Bohdan Rusyn, Volodymyr Minialo // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 251–254. — (Dynamic Data Mining & Data Stream Mining). |
| Bibliographic description (International): | Robust Approach to Estimation of the Intensity of Noisy Signal with Additive Uncorrelated Impulse Interference / Andriy Lozynskyy, Igor Romanyshyn, Bohdan Rusyn, Volodymyr Minialo // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 251–254. — (Dynamic Data Mining & Data Stream Mining). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | noisy signal additive uncorrelated impulse interference random signal parameters estimation robust method nonlinear filtering |
| Number of pages: | 4 |
| Page range: | 251-254 |
| Start page: | 251 |
| End page: | 254 |
| Abstract: | A robust approach to estimation the intensity of a noisy signal with additive uncorrelated impulse interference is proposed. An occurrence of the additive uncorrelated impulse interference leads to increasing of the observed signal dispersion within some sections with impulse interference. Robustness of the intensity estimation is achieved by decreasing the influence of sections with impulse interference. A number of nonlinear filtering methods basing on lower envelope detection are developed: two-parameter recursive filter, dilation filter, clipping derivative filter and filters based on order statistics. Proposed approach was approbated by a numerical simulation. Numerical simulation is validated the efficiency of the proposed approach for estimation the intensity of a noisy signal with additive uncorrelated impulse interference at dynamic data mining and data stream mining. |
| URI: | https://ena.lpnu.ua/handle/ntb/52502 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | https://feb.kuleuven.be/public/u0017833/PDFFILES/Croux_Dehon5.pdf http://www.rci.rutgers.edu/~dtyler/ShortCourse.pdf http://dx.doi.org/10.1016/j.jesp.2013.03.013 http://psystudy.ru https://www.mql5.com/ru/articles/346 |
| References (Ukraine): | [1] S. A. Ayvazyan, I. S. Enyukov, L. D. Mehsalkin, Applied statistics: Rudiments of simulation and data preprocessing. M.:Finances and Statistics, 1983. [2] P. J. Huber, Robust statistics. M.: Mir, 1984. (In Russian) [3] F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel, Robust statistics. M.: Mir, 1989. (In Russian) [4] J. W. Tukey, A survey of sampling from contaminated distributions. In: Contributions to Prob. and Statist. (Ed. Olkin I. et al.). Stanford Univ. Press. 1960, pp. 448–485. [5] A. W. F. Edwards, “Three Early Papers on Efficient Parametric Estimation,” Statistical Science, vol. 12, no. 1, pp. 35-47, 1997. [6] G. Shevlyakov, and P. Smirnov, “Robust Estimation of the Correlation Coefficient: an Attempt of Survey,” Austr. J. of Statistics, vol. 40, no.1&2, pp. 147-156, 2011. [7] P. O. Smirnov, Robust methods and algorithms of estimation the correlation data characteristics on the basis of new high-performance and rapid robust scale estimations. (Candidate dissertation). St. Petersburg, 2013. [8] C. Croux, and C. Dehon, Robust estimation of location and scale. Encyclopedia of Environmetrics, A.-H. El-Shaarawi and W. Piegorsch (eds). John Wiley & Sons Ltd: Chichester, UK, Retrieved from 2013. https://feb.kuleuven.be/public/u0017833/PDFFILES/Croux_Dehon5.pdf. [9] G. E. P. Box, “Non-Normality and Tests on Variance” Biometrika, vol. 40, pp. 318–335, 1953. [10] P. J. Bickel, and E. L. Lehmann, “Descriptive Statistics for nonparametric models. I.,” Introduction. The Annals of Statistics, vol. 3, no. 5, pp. 1038-1044, 1975. [11] P. J. Bickel, and E. L. Lehmann, “Descriptive Statistics for nonparametric models. II.,” Location. The Annals of Statistics, vol. 3, no. 5, pp. 1045-1069, 1975. [12] P. J. Bickel, and E. L. Lehmann, “Descriptive Statistics for nonparametric models. III.,” Dispersion. The Annals of Statistics, vol. 4, no. 6, pp. 1139-1158, 1976. [13] D. E. Tyler, A short course on robust statistics. Retrieved from http://www.rci.rutgers.edu/~dtyler/ShortCourse.pdf. [14] P. J. Rousseeuw, and C. Croux, “Alternatives to the Median Absolute Deviation,” Journal of the American Statistical Association, vol. 88, 424, pp. 1273-1283, 1993. [15] Christophe Leys, Christophe Ley UGent, Olivier Klein, Philippe Bernard and Laurent Licata, “Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median,” Journal of Experimental Social Psychology, vol. 49(4), pp.764-766, 2013. Retrieved from http://dx.doi.org/10.1016/j.jesp.2013.03.013. [16] A. Chakrabarty, “Large Deviations for Truncated heavy-tailed random variables: a boundary case,” Indian J. Pure Appl. Math., vol.48 (4), pp. 671-703, 2017. [17] R. A. Fisher, “On the Mathematical Foundations of Theoretical Statistics,” Phil. Trans. R. Soc. Lond. A., vol. 222, pp. 309-368, 1992. doi: 10.1098/rsta.1922.0009. [18] A. N. Kolmogorov, “The method of the median in the theory of errors,” Mathematical collect., vol.38, no. 3-4, pp. 47-50, 1931. [19] A. A. Lyubushin, Analysis of data from geophysical and environmental monitoring systems. М.: Nauka, 2007. [20] E. S. Gardner, Exponential smoothing: the state of the art. Part II. Houston, 2005. [21] Yu. S. Dodonov, and Yu. A. Dodonova, “Stable measures of central tendency: weighing as probable alternative of data truncation at the response time analysis,” Psychological researches, vol. 5(19), pp. 1–14, 2011. Retrieved from http://psystudy.ru. [22] Predicting time series using exponential smoothing. Retrieved from https://www.mql5.com/ru/articles/346. |
| References (International): | [1] S. A. Ayvazyan, I. S. Enyukov, L. D. Mehsalkin, Applied statistics: Rudiments of simulation and data preprocessing. M.:Finances and Statistics, 1983. [2] P. J. Huber, Robust statistics. M., Mir, 1984. (In Russian) [3] F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel, Robust statistics. M., Mir, 1989. (In Russian) [4] J. W. Tukey, A survey of sampling from contaminated distributions. In: Contributions to Prob. and Statist. (Ed. Olkin I. et al.). Stanford Univ. Press. 1960, pp. 448–485. [5] A. W. F. Edwards, "Three Early Papers on Efficient Parametric Estimation," Statistical Science, vol. 12, no. 1, pp. 35-47, 1997. [6] G. Shevlyakov, and P. Smirnov, "Robust Estimation of the Correlation Coefficient: an Attempt of Survey," Austr. J. of Statistics, vol. 40, no.1&2, pp. 147-156, 2011. [7] P. O. Smirnov, Robust methods and algorithms of estimation the correlation data characteristics on the basis of new high-performance and rapid robust scale estimations. (Candidate dissertation). St. Petersburg, 2013. [8] C. Croux, and C. Dehon, Robust estimation of location and scale. Encyclopedia of Environmetrics, A.-H. El-Shaarawi and W. Piegorsch (eds). John Wiley & Sons Ltd: Chichester, UK, Retrieved from 2013. https://feb.kuleuven.be/public/u0017833/PDFFILES/Croux_Dehon5.pdf. [9] G. E. P. Box, "Non-Normality and Tests on Variance" Biometrika, vol. 40, pp. 318–335, 1953. [10] P. J. Bickel, and E. L. Lehmann, "Descriptive Statistics for nonparametric models. I.," Introduction. The Annals of Statistics, vol. 3, no. 5, pp. 1038-1044, 1975. [11] P. J. Bickel, and E. L. Lehmann, "Descriptive Statistics for nonparametric models. II.," Location. The Annals of Statistics, vol. 3, no. 5, pp. 1045-1069, 1975. [12] P. J. Bickel, and E. L. Lehmann, "Descriptive Statistics for nonparametric models. III.," Dispersion. The Annals of Statistics, vol. 4, no. 6, pp. 1139-1158, 1976. [13] D. E. Tyler, A short course on robust statistics. Retrieved from http://www.rci.rutgers.edu/~dtyler/ShortCourse.pdf. [14] P. J. Rousseeuw, and C. Croux, "Alternatives to the Median Absolute Deviation," Journal of the American Statistical Association, vol. 88, 424, pp. 1273-1283, 1993. [15] Christophe Leys, Christophe Ley UGent, Olivier Klein, Philippe Bernard and Laurent Licata, "Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median," Journal of Experimental Social Psychology, vol. 49(4), pp.764-766, 2013. Retrieved from http://dx.doi.org/10.1016/j.jesp.2013.03.013. [16] A. Chakrabarty, "Large Deviations for Truncated heavy-tailed random variables: a boundary case," Indian J. Pure Appl. Math., vol.48 (4), pp. 671-703, 2017. [17] R. A. Fisher, "On the Mathematical Foundations of Theoretical Statistics," Phil. Trans. R. Soc. Lond. A., vol. 222, pp. 309-368, 1992. doi: 10.1098/rsta.1922.0009. [18] A. N. Kolmogorov, "The method of the median in the theory of errors," Mathematical collect., vol.38, no. 3-4, pp. 47-50, 1931. [19] A. A. Lyubushin, Analysis of data from geophysical and environmental monitoring systems. M., Nauka, 2007. [20] E. S. Gardner, Exponential smoothing: the state of the art. Part II. Houston, 2005. [21] Yu. S. Dodonov, and Yu. A. Dodonova, "Stable measures of central tendency: weighing as probable alternative of data truncation at the response time analysis," Psychological researches, vol. 5(19), pp. 1–14, 2011. Retrieved from http://psystudy.ru. [22] Predicting time series using exponential smoothing. Retrieved from https://www.mql5.com/ru/articles/346. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
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| 2018_Lozynskyy_A-Robust_Approach_to_Estimation_251-254.pdf | 221.09 kB | Adobe PDF | View/Open | |
| 2018_Lozynskyy_A-Robust_Approach_to_Estimation_251-254__COVER.png | 505.47 kB | image/png | View/Open |
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