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A0499
Title: Towards the development of arc length regression Authors:  Theodor Loots - University of Pretoria (South Africa) [presenting]
Andriette Bekker - University of Pretoria (South Africa)
Abstract: The coefficients obtained from using ordinary linear regression may be severely biased with corresponding estimates lacking accuracy when the assumptions of normality are not satisfied. A new framework is proposed where the arc lengths of the kernel density functions cast over the dependent and independent variables are matched in order to yield coefficient estimates. The significance of these estimates are evaluated using resampling techniques, and model selection performed by using entropy based measures such as the Bhattacharyya divergence measure and minimum description length principle (MDL).