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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Adaptive Estimation Of Heteroscedastic Linear Regression Model Using Probability Weighted Moments, Faqir Muhammad, Muhammad Aslam, G.R. Pasha
Adaptive Estimation Of Heteroscedastic Linear Regression Model Using Probability Weighted Moments, Faqir Muhammad, Muhammad Aslam, G.R. Pasha
Journal of Modern Applied Statistical Methods
An adaptive estimator is presented by using probability weighted moments as weights rather than conventional estimates of variances for unknown heteroscedastic errors while estimating a heteroscedastic linear regression model. Empirical studies of the data generated by simulations for normal, uniform, and logistically distributed error terms support our proposed estimator to be quite efficient, especially for small samples.
Least Squares Percentage Regression, Chris Tofallis
Least Squares Percentage Regression, Chris Tofallis
Journal of Modern Applied Statistical Methods
In prediction, the percentage error is often felt to be more meaningful than the absolute error. We therefore extend the method of least squares to deal with percentage errors, for both simple and multiple regression. Exact expressions are derived for the coefficients, and we show how such models can be estimated using standard software. When the relative error is normally distributed, least squares percentage regression is shown to provide maximum likelihood estimates. The multiplicative error model is linked to least squares percentage regression in the same way that the standard additive error model is linked to ordinary least squares regression.