A Monte Carlo simulation is used to compare estimation and inference procedures in least absolute value (LAV) and least squares (LS) regression models with asymmetric error distributions. Mean square errors (MSE) of coefficient estimates are used to assess the relative efficiency of the estimators. Hypothesis tests for coefficients are compared on the basis of empirical level of significance and power.
Dielman, Terry E.
"Least Absolute Value vs. Least Squares Estimation and Inference Procedures in Regression Models with Asymmetric Error Distributions,"
Journal of Modern Applied Statistical Methods:
1, Article 13.
Available at: http://digitalcommons.wayne.edu/jmasm/vol8/iss1/13