Robust Stability Best Subset Selection for Autocorrelated Data Based on Robust Location and Dispersion EstimatorReport as inadecuate




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Journal of Probability and StatisticsVolume 2015 2015, Article ID 432986, 8 pages

Research Article

Laboratory of Computational Statistics and Operations Research, INSPEM, University Putra Malaysia, 43400 Serdang, Malaysia

Department of Statistics, College of Administration and Economics, University of Al-Qadisiyah, Diwaniyah, Iraq

Faculty of Science and Institute for Mathematical Research, University Putra Malaysia, 43400 Serdang, Malaysia

Received 23 September 2015; Revised 7 December 2015; Accepted 8 December 2015

Academic Editor: Ramón M. Rodríguez-Dagnino

Copyright © 2015 Hassan S. Uraibi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.





Author: Hassan S. Uraibi, Habshah Midi, and Sohel Rana

Source: https://www.hindawi.com/



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