A Generalized Regression Methodology for Bivariate Heteroscedastic Data

Fernández Fernández, Antonio and Vázquez López, Manuel (2011). A Generalized Regression Methodology for Bivariate Heteroscedastic Data. "Communications In Statistics-Theory And Methods", v. 40 (n. 4); pp. 598-621. ISSN 0361-0926. https://doi.org/10.1080/03610920903444011.

Description

Title: A Generalized Regression Methodology for Bivariate Heteroscedastic Data
Author/s:
  • Fernández Fernández, Antonio
  • Vázquez López, Manuel
Item Type: Article
Título de Revista/Publicación: Communications In Statistics-Theory And Methods
Date: 2011
ISSN: 0361-0926
Volume: 40
Subjects:
Freetext Keywords: Errors in both axes, Heteroscedastic data, Linear regression
Faculty: E.U.I.T. Telecomunicación (UPM)
Department: Electrónica Física
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

We present a methodology for reducing a straight line fitting regression problem to a Least Squares minimization one. This is accomplished through the definition of a measure on the data space that takes into account directional dependences of errors, and the use of polar descriptors for straight lines. This strategy improves the robustness by avoiding singularities and non-describable lines. The methodology is powerful enough to deal with non-normal bivariate heteroscedastic data error models, but can also supersede classical regression methods by making some particular assumptions. An implementation of the methodology for the normal bivariate case is developed and evaluated.

More information

Item ID: 12253
DC Identifier: http://oa.upm.es/12253/
OAI Identifier: oai:oa.upm.es:12253
DOI: 10.1080/03610920903444011
Official URL: http://www.tandfonline.com/doi/abs/10.1080/03610920903444011
Deposited by: Memoria Investigacion
Deposited on: 10 Jan 2013 07:56
Last Modified: 21 Apr 2016 11:29
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