Saqr, Anwar and Khan, Shahjahan 
ORCID: https://orcid.org/0000-0002-0446-086X
(2014)
New weighted geometric mean method to estimate the slope of measurement error model.
Journal of Applied Statistical Science, 22 (3-4).
261- 280.
ISSN 1067-5817
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Text (Submitted Version)
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Abstract
This paper introduces a new weighted geometric mean (WG) estimator to fit regression line when both the response and explanatory variables are subject to measurement errors. The proposed estimator is based on the mathematical relationship between the vertical and orthogonal distances of the observed points and the regression line (cf. Saqr and Khan, 2012). It minimizes the orthogonal distance of the observed points from the unfitted line. The WG estimator is less sensitive to the ratio of error variances. It is a better alternative than the currently used geometric mean (GM) and OLS-bisector estimators. Extensive simulation results show that the proposed WG estimator is much more stable than the geometric mean and OLS-bisector estimators. The mean absolute error of the WG estimator is consistently smaller than the geometric mean and OLS-bisector estimators.
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| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Permanent restricted access to Published version, in accordance with the copyright policy of the publisher. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Date Deposited: | 23 Nov 2017 00:31 |
| Last Modified: | 16 May 2018 05:04 |
| Uncontrolled Keywords: | linear regression models; measurement error models; re ection of points; ratio of error variances; geometric mean estimator; OLS-bisector |
| Fields of Research (2008): | 01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics 01 Mathematical Sciences > 0104 Statistics > 010499 Statistics not elsewhere classified 01 Mathematical Sciences > 0104 Statistics > 010405 Statistical Theory |
| Fields of Research (2020): | 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490501 Applied statistics 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490599 Statistics not elsewhere classified 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490509 Statistical theory |
| Socio-Economic Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences |
| URI: | http://eprints.usq.edu.au/id/eprint/30461 |
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