Robust Stability Best Subset Selection for Autocorrelated Data Based on Robust Location and Dispersion Estimator
Stability selection (multisplit) approach is a variable selection procedure which relies on multisplit data to overcome the shortcomings that may occur to single-split data. Unfortunately, this procedure yields very poor results in the presence of outliers and other contamination in the original dat...
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Language: | English |
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Wiley
2015-01-01
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Series: | Journal of Probability and Statistics |
Online Access: | http://dx.doi.org/10.1155/2015/432986 |
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author | Hassan S. Uraibi Habshah Midi Sohel Rana |
author_facet | Hassan S. Uraibi Habshah Midi Sohel Rana |
author_sort | Hassan S. Uraibi |
collection | DOAJ |
description | Stability selection (multisplit) approach is a variable selection procedure which relies on multisplit data to overcome the shortcomings that may occur to single-split data. Unfortunately, this procedure yields very poor results in the presence of outliers and other contamination in the original data. The problem becomes more complicated when the regression residuals are serially correlated. This paper presents a new robust stability selection procedure to remedy the combined problem of autocorrelation and outliers. We demonstrate the good performance of our proposed robust selection method using real air quality data and simulation study. |
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id | doaj-art-f588ed408e8640ca8b53d722b4f4cf7c |
institution | Kabale University |
issn | 1687-952X 1687-9538 |
language | English |
publishDate | 2015-01-01 |
publisher | Wiley |
record_format | Article |
series | Journal of Probability and Statistics |
spelling | doaj-art-f588ed408e8640ca8b53d722b4f4cf7c2025-02-03T06:44:35ZengWileyJournal of Probability and Statistics1687-952X1687-95382015-01-01201510.1155/2015/432986432986Robust Stability Best Subset Selection for Autocorrelated Data Based on Robust Location and Dispersion EstimatorHassan S. Uraibi0Habshah Midi1Sohel Rana2Laboratory of Computational Statistics and Operations Research, INSPEM, University Putra Malaysia, 43400 Serdang, MalaysiaFaculty of Science and Institute for Mathematical Research, University Putra Malaysia, 43400 Serdang, MalaysiaFaculty of Science and Institute for Mathematical Research, University Putra Malaysia, 43400 Serdang, MalaysiaStability selection (multisplit) approach is a variable selection procedure which relies on multisplit data to overcome the shortcomings that may occur to single-split data. Unfortunately, this procedure yields very poor results in the presence of outliers and other contamination in the original data. The problem becomes more complicated when the regression residuals are serially correlated. This paper presents a new robust stability selection procedure to remedy the combined problem of autocorrelation and outliers. We demonstrate the good performance of our proposed robust selection method using real air quality data and simulation study.http://dx.doi.org/10.1155/2015/432986 |
spellingShingle | Hassan S. Uraibi Habshah Midi Sohel Rana Robust Stability Best Subset Selection for Autocorrelated Data Based on Robust Location and Dispersion Estimator Journal of Probability and Statistics |
title | Robust Stability Best Subset Selection for Autocorrelated Data Based on Robust Location and Dispersion Estimator |
title_full | Robust Stability Best Subset Selection for Autocorrelated Data Based on Robust Location and Dispersion Estimator |
title_fullStr | Robust Stability Best Subset Selection for Autocorrelated Data Based on Robust Location and Dispersion Estimator |
title_full_unstemmed | Robust Stability Best Subset Selection for Autocorrelated Data Based on Robust Location and Dispersion Estimator |
title_short | Robust Stability Best Subset Selection for Autocorrelated Data Based on Robust Location and Dispersion Estimator |
title_sort | robust stability best subset selection for autocorrelated data based on robust location and dispersion estimator |
url | http://dx.doi.org/10.1155/2015/432986 |
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