Weighted reduced major axis method for regression model

Saqr, Anwar and Khan, Shahjahan ORCID: https://orcid.org/0000-0002-0446-086X (2012) Weighted reduced major axis method for regression model. In: 12th Islamic Countries Conference on Statistical Sciences (ICCS 2012): Statistics for Everyone and Everywhere, 19-22 Dec 2012, Doha, Qatar.

Text (Documentation)

Download (244kB) | Preview
Text (Published Version)

Download (1MB) | Preview


The reduced major axis (RMA) method is widely used in many disciplines as a solution to errors in variables model, although it lacks efficiency. This paper provides an alternative view on RMA estimator. Moreover, it introduces a new estimator to fit regression line when both variables are subject to measurement errors. The proposed weighted reduced major axis (WR) estimator is derived based on the mathematical relationship between the vertical and orthogonal distances of the observed points and the regression line. Compared to the RMA and OLS-bisector estimators the proposed WR estimator is less sensitive to the variation of the ratio of error variances. The simulation results show that the WR estimator is more consistent and efficient than the RMA and OLS-bisector estimators.

Statistics for USQ ePrint 24210
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2013 Islamic Countries Society of Statistical Sciences.
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Date Deposited: 29 Oct 2013 00:14
Last Modified: 27 Jun 2017 05:27
Uncontrolled Keywords: linear regression models; measurement error models; reflection of points; ratio of error variances; OLS-bisector
Fields of Research (2008): 01 Mathematical Sciences > 0104 Statistics > 010402 Biostatistics
01 Mathematical Sciences > 0101 Pure Mathematics > 010111 Real and Complex Functions (incl. Several Variables)
01 Mathematical Sciences > 0102 Applied Mathematics > 010203 Calculus of Variations, Systems Theory and Control Theory
Fields of Research (2020): 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490502 Biostatistics
49 MATHEMATICAL SCIENCES > 4904 Pure mathematics > 490411 Real and complex functions (incl. several variables)
49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490103 Calculus of variations, mathematical aspects of systems theory and control 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/24210

Actions (login required)

View Item Archive Repository Staff Only