Обнаружение аномальных измерений при обработке данных малого объема
This article describes the criteria for detection of outliers power depending on a small size sample. Removing outliers is one of the stages of signals pre-processing. A statistical experiment, in which using a random number generator were received arrays of data, containing several thousand samples...
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| Дата: | 2016 |
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| Формат: | Стаття |
| Мова: | Ukrainian |
| Опубліковано: |
PE "Politekhperiodika", Book and Journal Publishers
2016
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| Онлайн доступ: | https://www.tkea.com.ua/index.php/journal/article/view/TKEA2016.4-5.42 |
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| Назва журналу: | Technology and design in electronic equipment |
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Technology and design in electronic equipment| id |
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oai:tkea.com.ua:article-2352025-05-30T19:31:10Z Detection of outliers in processing of small size data Обнаружение аномальных измерений при обработке данных малого объема Popukaylo, V. S. small size data outlier detection criteria anomalous meterages outlier analysis малая выборка грубые ошибки аномальные измерения критерии обнаружения выбросов This article describes the criteria for detection of outliers power depending on a small size sample. Removing outliers is one of the stages of signals pre-processing. A statistical experiment, in which using a random number generator were received arrays of data, containing several thousand samples with normal distribution, with the given mean averages and standard deviation for each n-value, was conducted to solve this problem. Thus, we researched and vividly illustrated the possibility of Grubbs, Dixon, Tietjen — Moore, Irving, Chauvenet, Lvovsky and Romanovsky criteria at studied data sizes from 5 to 20 meterages. Conclusions about the applicability of each criterion for the outliersdetection in processing of small size data were made. Lvovsky criterion was recognized the optimal criterion. Dixon’s criterion was recommended for n ≤ 10. Irwin’s criterion was recommended when n ≥ 10. Tietjen—Moore’scriterion can be recommended for the detection of outliers in small samples for n > 5, since it recognizes errors well in the values of a ¯x + 4σ and has the least amount of I type mistakes. Grubb’s with an unknown standard deviation may be used in samples for n ≥ 15. Chauvenet and Romanovsky criteria cannot be recommended for the detection of outliers in small size data. Рассмотрена мощность критериев обнаружения аномальных измерений в зависимости от объема малой выборки. Исследованы и наглядно проиллюстрированы возможности критериев Граббса, Диксона, Титьена — Мура, Ирвина, Шовене, Львовского и Романовского при объеме исследуемых данных от 5 до 20 измерений. Сделаны выводы о возможности применения каждого из критериев для обнаружения аномальных измерений при обработке данных малого объема. PE "Politekhperiodika", Book and Journal Publishers 2016-10-29 Article Article Peer-reviewed Article application/pdf https://www.tkea.com.ua/index.php/journal/article/view/TKEA2016.4-5.42 10.15222/TKEA2016.4-5.42 Technology and design in electronic equipment; No. 4–5 (2016): Tekhnologiya i konstruirovanie v elektronnoi apparature; 42-46 Технологія та конструювання в електронній апаратурі; № 4–5 (2016): Технология и конструирование в электронной аппаратуре; 42-46 3083-6549 3083-6530 uk https://www.tkea.com.ua/index.php/journal/article/view/TKEA2016.4-5.42/204 Copyright (c) 2016 V. S. Popukaylo http://creativecommons.org/licenses/by/4.0/ |
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Technology and design in electronic equipment |
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| datestamp_date |
2025-05-30T19:31:10Z |
| collection |
OJS |
| language |
Ukrainian |
| topic |
малая выборка грубые ошибки аномальные измерения критерии обнаружения выбросов |
| spellingShingle |
малая выборка грубые ошибки аномальные измерения критерии обнаружения выбросов Popukaylo, V. S. Обнаружение аномальных измерений при обработке данных малого объема |
| topic_facet |
small size data outlier detection criteria anomalous meterages outlier analysis малая выборка грубые ошибки аномальные измерения критерии обнаружения выбросов |
| format |
Article |
| author |
Popukaylo, V. S. |
| author_facet |
Popukaylo, V. S. |
| author_sort |
Popukaylo, V. S. |
| title |
Обнаружение аномальных измерений при обработке данных малого объема |
| title_short |
Обнаружение аномальных измерений при обработке данных малого объема |
| title_full |
Обнаружение аномальных измерений при обработке данных малого объема |
| title_fullStr |
Обнаружение аномальных измерений при обработке данных малого объема |
| title_full_unstemmed |
Обнаружение аномальных измерений при обработке данных малого объема |
| title_sort |
обнаружение аномальных измерений при обработке данных малого объема |
| title_alt |
Detection of outliers in processing of small size data |
| description |
This article describes the criteria for detection of outliers power depending on a small size sample. Removing outliers is one of the stages of signals pre-processing. A statistical experiment, in which using a random number generator were received arrays of data, containing several thousand samples with normal distribution, with the given mean averages and standard deviation for each n-value, was conducted to solve this problem. Thus, we researched and vividly illustrated the possibility of Grubbs, Dixon, Tietjen — Moore, Irving, Chauvenet, Lvovsky and Romanovsky criteria at studied data sizes from 5 to 20 meterages. Conclusions about the applicability of each criterion for the outliersdetection in processing of small size data were made. Lvovsky criterion was recognized the optimal criterion. Dixon’s criterion was recommended for n ≤ 10. Irwin’s criterion was recommended when n ≥ 10. Tietjen—Moore’scriterion can be recommended for the detection of outliers in small samples for n > 5, since it recognizes errors well in the values of a ¯x + 4σ and has the least amount of I type mistakes. Grubb’s with an unknown standard deviation may be used in samples for n ≥ 15. Chauvenet and Romanovsky criteria cannot be recommended for the detection of outliers in small size data. |
| publisher |
PE "Politekhperiodika", Book and Journal Publishers |
| publishDate |
2016 |
| url |
https://www.tkea.com.ua/index.php/journal/article/view/TKEA2016.4-5.42 |
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AT popukaylovs detectionofoutliersinprocessingofsmallsizedata AT popukaylovs obnaruženieanomalʹnyhizmerenijpriobrabotkedannyhmalogoobʺema |
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2025-09-24T17:30:35Z |
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2025-09-24T17:30:35Z |
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