A Generalized Regression Methodology for Bivariate Heteroscedastic Data

作者：Fernandez Antonio; Vazquez Manuel^*

来源：Communications in Statistics - Theory and Methods, 2011, 40(4): 598-621.

DOI：10.1080/03610920903444011

• Errors in both axes
• Heteroscedastic data
• Linear regression
• 62J02

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.

• 出版日期2011