Skip to main content

Multivariate Generalization of Reduced Major Axis Regression


Abstract A least total area of triangle method was proposed by Teissier (1948) for fitting a straight line to data from a pair of variables without treating either variable as the dependent variable while allowing each of the variables to have measurement errors. This method is commonly called Reduced Major Axis (RMA) regression and is often used instead of Ordinary Least Squares (OLS) regression. Results for confidence intervals, hypothesis testing and asymptotic distributions of coefficient estimates in the bivariate case are reviewed. A generalization of RMA to more than two variables for fitting a plane to data is obtained by minimizing the sum of a function of the volumes obtained by drawing, from each data point, lines parallel to each coordin... (more)
Created Date 2012
Contributor Li, Jingjin (Author) / Young, Dennis (Advisor) / Eubank, Randall (Advisor) / Reiser, Mark (Committee member) / Kao, Ming-Hung (Committee member) / Yang, Yan (Committee member) / Arizona State University (Publisher)
Subject Statistics / OLS / Reduced Major Axis / Regression
Type Doctoral Dissertation
Extent 258 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Ph.D. Statistics 2012
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


  Full Text
3.6 MB application/pdf
Download Count: 3007

Description Dissertation/Thesis