Outliers are observations that have extreme value relations. Herewith leverage is a measure of how an independent variable deviates from its mean. An observation with an extreme value on a predictor variable is a point with high leverage. The presence of outliers can lead to inflated error rates and substantial distortions of parameter and statistic estimates when using either parametric or nonparametric tests. Casual observation of the literature suggests that researchers rarely report checking for outliers of any sort and taking remedial measures for outliers. Outliers can have positive deleterious effects on statistical analyses. For instance, they serve to increase error variance and reduce the power of statistical tests; they can decrease normality, altering the odds of making both Type I and Type II errors for non- randomly distributed; and they can seriously bias or influence estimates that may be of substantive interest. These outliers are cased from incorrect recording data, intentional or motivated mis-reporting, sampling error and Outliers as legitimate cases sampled from the correct population. According to some literatures; Point outliers, Contextual Outliers and Collective Outliers are the three types of outliers. Robust regression estimators can be a powerful tool for detection and identifying outliers in complicated data sets. Robust regression, deals with the problem of outliers in a regression and produce different coefficient estimates than OLS does.
| DOI | 10.11648/j.ijssam.20200501.12 |
| Published in | International Journal of Systems Science and Applied Mathematics (Volume 5, Issue 1, March 2020) |
| Page(s) | 4-11 |
| 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. |
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Copyright © The Author(s), 2024. Published by Science Publishing Group |
Break Down Point, Leverage Points, M-estimation, Outlier, Robust Regression Model
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APA Style
Getnet Bogale Begashaw, Yordanos Berihun Yohannes. (2020). Review of Outlier Detection and Identifying Using Robust Regression Model. International Journal of Systems Science and Applied Mathematics, 5(1), 4-11. https://doi.org/10.11648/j.ijssam.20200501.12
ACS Style
Getnet Bogale Begashaw; Yordanos Berihun Yohannes. Review of Outlier Detection and Identifying Using Robust Regression Model. Int. J. Syst. Sci. Appl. Math. 2020, 5(1), 4-11. doi: 10.11648/j.ijssam.20200501.12
AMA Style
Getnet Bogale Begashaw, Yordanos Berihun Yohannes. Review of Outlier Detection and Identifying Using Robust Regression Model. Int J Syst Sci Appl Math. 2020;5(1):4-11. doi: 10.11648/j.ijssam.20200501.12
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Download@article{10.11648/j.ijssam.20200501.12,
author = {Getnet Bogale Begashaw and Yordanos Berihun Yohannes},
title = {Review of Outlier Detection and Identifying Using Robust Regression Model},
journal = {International Journal of Systems Science and Applied Mathematics},
volume = {5},
number = {1},
pages = {4-11},
doi = {10.11648/j.ijssam.20200501.12},
url = {https://doi.org/10.11648/j.ijssam.20200501.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20200501.12},
abstract = {Outliers are observations that have extreme value relations. Herewith leverage is a measure of how an independent variable deviates from its mean. An observation with an extreme value on a predictor variable is a point with high leverage. The presence of outliers can lead to inflated error rates and substantial distortions of parameter and statistic estimates when using either parametric or nonparametric tests. Casual observation of the literature suggests that researchers rarely report checking for outliers of any sort and taking remedial measures for outliers. Outliers can have positive deleterious effects on statistical analyses. For instance, they serve to increase error variance and reduce the power of statistical tests; they can decrease normality, altering the odds of making both Type I and Type II errors for non- randomly distributed; and they can seriously bias or influence estimates that may be of substantive interest. These outliers are cased from incorrect recording data, intentional or motivated mis-reporting, sampling error and Outliers as legitimate cases sampled from the correct population. According to some literatures; Point outliers, Contextual Outliers and Collective Outliers are the three types of outliers. Robust regression estimators can be a powerful tool for detection and identifying outliers in complicated data sets. Robust regression, deals with the problem of outliers in a regression and produce different coefficient estimates than OLS does.},
year = {2020}
}
TY - JOUR T1 - Review of Outlier Detection and Identifying Using Robust Regression Model AU - Getnet Bogale Begashaw AU - Yordanos Berihun Yohannes Y1 - 2020/04/13 PY - 2020 N1 - https://doi.org/10.11648/j.ijssam.20200501.12 DO - 10.11648/j.ijssam.20200501.12 T2 - International Journal of Systems Science and Applied Mathematics JF - International Journal of Systems Science and Applied Mathematics JO - International Journal of Systems Science and Applied Mathematics SP - 4 EP - 11 PB - Science Publishing Group SN - 2575-5803 UR - https://doi.org/10.11648/j.ijssam.20200501.12 AB - Outliers are observations that have extreme value relations. Herewith leverage is a measure of how an independent variable deviates from its mean. An observation with an extreme value on a predictor variable is a point with high leverage. The presence of outliers can lead to inflated error rates and substantial distortions of parameter and statistic estimates when using either parametric or nonparametric tests. Casual observation of the literature suggests that researchers rarely report checking for outliers of any sort and taking remedial measures for outliers. Outliers can have positive deleterious effects on statistical analyses. For instance, they serve to increase error variance and reduce the power of statistical tests; they can decrease normality, altering the odds of making both Type I and Type II errors for non- randomly distributed; and they can seriously bias or influence estimates that may be of substantive interest. These outliers are cased from incorrect recording data, intentional or motivated mis-reporting, sampling error and Outliers as legitimate cases sampled from the correct population. According to some literatures; Point outliers, Contextual Outliers and Collective Outliers are the three types of outliers. Robust regression estimators can be a powerful tool for detection and identifying outliers in complicated data sets. Robust regression, deals with the problem of outliers in a regression and produce different coefficient estimates than OLS does. VL - 5 IS - 1 ER -
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