The technique of a finding of distribution functions of an absolute maximum of non-Gaussian random processes has been illustrated. On an example of Hoyt process the limiting distribution laws of its absolute maximum have been found. By methods of statistical modeling it has been established that the given asymptotic approximations ensure a satisfactory description of the true distributions over a wide range of parameter values of the random process
| Published in | American Journal of Theoretical and Applied Statistics (Volume 2, Issue 3) |
| DOI | 10.11648/j.ajtas.20130203.13 |
| Page(s) | 54-60 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2013. Published by Science Publishing Group |
Differentiable and Nondifferentiable Random Process, Distribution Function of the Absolute Maximum, Outliers of the Random Process, Statistical Modeling
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APA Style
O. V. Chernoyarov, A. V. Salnikova, Ya. A. Kupriyanova. (2013). Definition of Probability Characteristics of the Absolute Maximum of Non-Gaussian Random Processes by Example of Hoyt Process. American Journal of Theoretical and Applied Statistics, 2(3), 54-60. https://doi.org/10.11648/j.ajtas.20130203.13
ACS Style
O. V. Chernoyarov; A. V. Salnikova; Ya. A. Kupriyanova. Definition of Probability Characteristics of the Absolute Maximum of Non-Gaussian Random Processes by Example of Hoyt Process. Am. J. Theor. Appl. Stat. 2013, 2(3), 54-60. doi: 10.11648/j.ajtas.20130203.13
AMA Style
O. V. Chernoyarov, A. V. Salnikova, Ya. A. Kupriyanova. Definition of Probability Characteristics of the Absolute Maximum of Non-Gaussian Random Processes by Example of Hoyt Process. Am J Theor Appl Stat. 2013;2(3):54-60. doi: 10.11648/j.ajtas.20130203.13
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Download@article{10.11648/j.ajtas.20130203.13,
author = {O. V. Chernoyarov and A. V. Salnikova and Ya. A. Kupriyanova},
title = {Definition of Probability Characteristics of the Absolute Maximum of Non-Gaussian Random Processes by Example of Hoyt Process},
journal = {American Journal of Theoretical and Applied Statistics},
volume = {2},
number = {3},
pages = {54-60},
doi = {10.11648/j.ajtas.20130203.13},
url = {https://doi.org/10.11648/j.ajtas.20130203.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20130203.13},
abstract = {The technique of a finding of distribution functions of an absolute maximum of non-Gaussian random processes has been illustrated. On an example of Hoyt process the limiting distribution laws of its absolute maximum have been found. By methods of statistical modeling it has been established that the given asymptotic approximations ensure a satisfactory description of the true distributions over a wide range of parameter values of the random process},
year = {2013}
}
TY - JOUR T1 - Definition of Probability Characteristics of the Absolute Maximum of Non-Gaussian Random Processes by Example of Hoyt Process AU - O. V. Chernoyarov AU - A. V. Salnikova AU - Ya. A. Kupriyanova Y1 - 2013/05/30 PY - 2013 N1 - https://doi.org/10.11648/j.ajtas.20130203.13 DO - 10.11648/j.ajtas.20130203.13 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 54 EP - 60 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20130203.13 AB - The technique of a finding of distribution functions of an absolute maximum of non-Gaussian random processes has been illustrated. On an example of Hoyt process the limiting distribution laws of its absolute maximum have been found. By methods of statistical modeling it has been established that the given asymptotic approximations ensure a satisfactory description of the true distributions over a wide range of parameter values of the random process VL - 2 IS - 3 ER -
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