DC Field | Value | Language |
dc.contributor.author | Lozynskyy, Andriy | |
dc.contributor.author | Romanyshyn, Igor | |
dc.contributor.author | Rusyn, Bohdan | |
dc.contributor.author | Minialo, Volodymyr | |
dc.coverage.temporal | 21-25 August 2018, Lviv | |
dc.date.accessioned | 2020-06-19T12:05:22Z | - |
dc.date.available | 2020-06-19T12:05:22Z | - |
dc.date.created | 2018-02-28 | |
dc.date.issued | 2018-02-28 | |
dc.identifier.citation | 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). | |
dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/52502 | - |
dc.description.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. | |
dc.format.extent | 251-254 | |
dc.language.iso | en | |
dc.publisher | Lviv Politechnic Publishing House | |
dc.relation.ispartof | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 | |
dc.relation.uri | https://feb.kuleuven.be/public/u0017833/PDFFILES/Croux_Dehon5.pdf | |
dc.relation.uri | http://www.rci.rutgers.edu/~dtyler/ShortCourse.pdf | |
dc.relation.uri | http://dx.doi.org/10.1016/j.jesp.2013.03.013 | |
dc.relation.uri | http://psystudy.ru | |
dc.relation.uri | https://www.mql5.com/ru/articles/346 | |
dc.subject | noisy signal | |
dc.subject | additive uncorrelated impulse interference | |
dc.subject | random signal parameters estimation | |
dc.subject | robust method | |
dc.subject | nonlinear filtering | |
dc.title | Robust Approach to Estimation of the Intensity of Noisy Signal with Additive Uncorrelated Impulse Interference | |
dc.type | Conference Abstract | |
dc.rights.holder | © Національний університет “Львівська політехніка”, 2018 | |
dc.contributor.affiliation | Karpenko Phisico-Mechanical Institute NAS of Ukraine | |
dc.format.pages | 4 | |
dc.identifier.citationen | 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). | |
dc.relation.references | [1] S. A. Ayvazyan, I. S. Enyukov, L. D. Mehsalkin, Applied statistics: Rudiments of simulation and data preprocessing. M.:Finances and Statistics, 1983. | |
dc.relation.references | [2] P. J. Huber, Robust statistics. M.: Mir, 1984. (In Russian) | |
dc.relation.references | [3] F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel, Robust statistics. M.: Mir, 1989. (In Russian) | |
dc.relation.references | [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. | |
dc.relation.references | [5] A. W. F. Edwards, “Three Early Papers on Efficient Parametric Estimation,” Statistical Science, vol. 12, no. 1, pp. 35-47, 1997. | |
dc.relation.references | [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. | |
dc.relation.references | [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. | |
dc.relation.references | [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. | |
dc.relation.references | [9] G. E. P. Box, “Non-Normality and Tests on Variance” Biometrika, vol. 40, pp. 318–335, 1953. | |
dc.relation.references | [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. | |
dc.relation.references | [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. | |
dc.relation.references | [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. | |
dc.relation.references | [13] D. E. Tyler, A short course on robust statistics. Retrieved from http://www.rci.rutgers.edu/~dtyler/ShortCourse.pdf. | |
dc.relation.references | [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. | |
dc.relation.references | [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. | |
dc.relation.references | [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. | |
dc.relation.references | [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. | |
dc.relation.references | [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. | |
dc.relation.references | [19] A. A. Lyubushin, Analysis of data from geophysical and environmental monitoring systems. М.: Nauka, 2007. | |
dc.relation.references | [20] E. S. Gardner, Exponential smoothing: the state of the art. Part II. Houston, 2005. | |
dc.relation.references | [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. | |
dc.relation.references | [22] Predicting time series using exponential smoothing. Retrieved from https://www.mql5.com/ru/articles/346. | |
dc.relation.referencesen | [1] S. A. Ayvazyan, I. S. Enyukov, L. D. Mehsalkin, Applied statistics: Rudiments of simulation and data preprocessing. M.:Finances and Statistics, 1983. | |
dc.relation.referencesen | [2] P. J. Huber, Robust statistics. M., Mir, 1984. (In Russian) | |
dc.relation.referencesen | [3] F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel, Robust statistics. M., Mir, 1989. (In Russian) | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [5] A. W. F. Edwards, "Three Early Papers on Efficient Parametric Estimation," Statistical Science, vol. 12, no. 1, pp. 35-47, 1997. | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [9] G. E. P. Box, "Non-Normality and Tests on Variance" Biometrika, vol. 40, pp. 318–335, 1953. | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [13] D. E. Tyler, A short course on robust statistics. Retrieved from http://www.rci.rutgers.edu/~dtyler/ShortCourse.pdf. | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [19] A. A. Lyubushin, Analysis of data from geophysical and environmental monitoring systems. M., Nauka, 2007. | |
dc.relation.referencesen | [20] E. S. Gardner, Exponential smoothing: the state of the art. Part II. Houston, 2005. | |
dc.relation.referencesen | [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. | |
dc.relation.referencesen | [22] Predicting time series using exponential smoothing. Retrieved from https://www.mql5.com/ru/articles/346. | |
dc.citation.conference | IEEE second international conference "Data stream mining and processing" | |
dc.citation.spage | 251 | |
dc.citation.epage | 254 | |
dc.coverage.placename | Львів | |
Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference
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