Open Access. Powered by Scholars. Published by Universities.®
Social and Behavioral Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Social and Behavioral Sciences
Least Absolute Value Vs. Least Squares Estimation And Inference Procedures In Regression Models With Asymmetric Error Distributions, Terry E. Dielman
Least Absolute Value Vs. Least Squares Estimation And Inference Procedures In Regression Models With Asymmetric Error Distributions, Terry E. Dielman
Journal of Modern Applied Statistical Methods
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.
A Monte Carlo Comparison Of Regression Estimators When The Error Distribution Is Long-Tailed Symmetric, Oya Can Mutan, Birdal Şenoğlu
A Monte Carlo Comparison Of Regression Estimators When The Error Distribution Is Long-Tailed Symmetric, Oya Can Mutan, Birdal Şenoğlu
Journal of Modern Applied Statistical Methods
The performances of the ordinary least squares (OLS), modified maximum likelihood (MML), least absolute deviations (LAD), Winsorized least squares (WIN), trimmed least squares (TLS), Theil’s (Theil) and weighted Theil’s (Weighted Theil) estimators are compared under the simple linear regression model in terms of their bias and efficiency when the distribution of error terms is long-tailed symmetric.