A Note on the Performance of Biased Estimators with Autocorrelated Errors

It is a well-established fact in regression analysis that multicollinearity and autocorrelated errors have adverse effects on the properties of the least squares estimator. Huang and Yang (2015) and Chandra and Tyagi (2016) studied the PCTP estimator and the r-(k,d) class estimator, respectively, to...

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Main Authors: Gargi Tyagi, Shalini Chandra
Format: Article
Language:English
Published: Wiley 2017-01-01
Series:International Journal of Mathematics and Mathematical Sciences
Online Access:http://dx.doi.org/10.1155/2017/2045653
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author Gargi Tyagi
Shalini Chandra
author_facet Gargi Tyagi
Shalini Chandra
author_sort Gargi Tyagi
collection DOAJ
description It is a well-established fact in regression analysis that multicollinearity and autocorrelated errors have adverse effects on the properties of the least squares estimator. Huang and Yang (2015) and Chandra and Tyagi (2016) studied the PCTP estimator and the r-(k,d) class estimator, respectively, to deal with both problems simultaneously and compared their performances with the estimators obtained as their special cases. However, to the best of our knowledge, the performance of both estimators has not been compared so far. Hence, this paper is intended to compare the performance of these two estimators under mean squared error (MSE) matrix criterion. Further, a simulation study is conducted to evaluate superiority of the r-(k,d) class estimator over the PCTP estimator by means of percentage relative efficiency. Furthermore, two numerical examples have been given to illustrate the performance of the estimators.
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institution Kabale University
issn 0161-1712
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series International Journal of Mathematics and Mathematical Sciences
spelling doaj-art-b567000fc2514184a3c62003a241bf782025-02-03T06:07:04ZengWileyInternational Journal of Mathematics and Mathematical Sciences0161-17121687-04252017-01-01201710.1155/2017/20456532045653A Note on the Performance of Biased Estimators with Autocorrelated ErrorsGargi Tyagi0Shalini Chandra1Department of Mathematics & Statistics, Banasthali University, Rajasthan 304022, IndiaDepartment of Mathematics & Statistics, Banasthali University, Rajasthan 304022, IndiaIt is a well-established fact in regression analysis that multicollinearity and autocorrelated errors have adverse effects on the properties of the least squares estimator. Huang and Yang (2015) and Chandra and Tyagi (2016) studied the PCTP estimator and the r-(k,d) class estimator, respectively, to deal with both problems simultaneously and compared their performances with the estimators obtained as their special cases. However, to the best of our knowledge, the performance of both estimators has not been compared so far. Hence, this paper is intended to compare the performance of these two estimators under mean squared error (MSE) matrix criterion. Further, a simulation study is conducted to evaluate superiority of the r-(k,d) class estimator over the PCTP estimator by means of percentage relative efficiency. Furthermore, two numerical examples have been given to illustrate the performance of the estimators.http://dx.doi.org/10.1155/2017/2045653
spellingShingle Gargi Tyagi
Shalini Chandra
A Note on the Performance of Biased Estimators with Autocorrelated Errors
International Journal of Mathematics and Mathematical Sciences
title A Note on the Performance of Biased Estimators with Autocorrelated Errors
title_full A Note on the Performance of Biased Estimators with Autocorrelated Errors
title_fullStr A Note on the Performance of Biased Estimators with Autocorrelated Errors
title_full_unstemmed A Note on the Performance of Biased Estimators with Autocorrelated Errors
title_short A Note on the Performance of Biased Estimators with Autocorrelated Errors
title_sort note on the performance of biased estimators with autocorrelated errors
url http://dx.doi.org/10.1155/2017/2045653
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