Understanding Least Absolute Value in Regression-Based Data Mining

Understanding Least Absolute Value in Regression-based Data Mining

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 compar...

Least absolute value regression: recent contributions

This article provides a review of research involving least absolute value (LAV) regression. The review is concentrated primarily on research publisbed since Ihe sur\'ey article by Dielman (Dielman, T. E. (1984). lx"a.sl absolute value estimation in regression mtxlels; An annotated bibliography. Communications ill Statistics Theory and Methoih. 4. 513-541.) and includes articles on LAV estimatio...

Regression Model Estimation Using Least Absolute Deviations , Least Squares Deviations and Minimax Absolute Deviations Criteria

Regression models and their statistical analyses is the most important tool used by scientists in data analyses especially for modeling the relationship among random variables and making predictions with higher accuracy. A fundamental problem in the theory of errors, which has drawn attention of leading mathematicians and scientists since past few centuries, was that of fitting functions. For t...

Least Absolute Value vs. Least Squares Estimation and Inference Procedures in Regression Models with Asymmetric Error Distributions

Solve least absolute value regression problems using modified goal programming techniques

Scope and PurposeÐLeast absolute value (LAV) regression methods have been widely applied in estimating regression equations. However, most of the current LAV methods are based on the original goal program developed over four decades. On the basis of a modi®ed goal program, this study reformulates the LAV problem using a markedly lower number of deviational variables than used in the current LAV...