Understanding Least Absolute Value in Regression-based Data Mining

Abstract

This article advances our understanding of regression-based data mining by comparing the utility of Least Absolute Value (LAV) and Least Squares (LS) regression methods. Using demographic variables from U.S. state-wide data, we fit variable regression models to dependent variables of varying distributions using both LS and LAV. Forecasts generated from the resulting equations are used to compare the performance of the regression methods under different dependent variable distribution conditions. Initial findings indicate LAV procedures better forecast in data mining applications when the dependent variable is non-normal. Our results differ from those found in prior research using simulated data.

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Cite this paper

@inproceedings{Wimble2016UnderstandingLA, title={Understanding Least Absolute Value in Regression-based Data Mining}, author={Matt Wimble and Michele Yoder and Young K. Ro}, year={2016} }