Comparison of regression techniques via Monte Carlo simulation

Date

2004

Author

Mutan, Oya Can

Metadata

Show full item record

Item Usage Stats

2
views

1
downloads

The ordinary least squares (OLS) is one of the most widely used methods for modelling the functional relationship between variables. However, this estimation procedure counts on some assumptions and the violation of these assumptions may lead to nonrobust estimates. In this study, the simple linear regression model is investigated for conditions in which the distribution of the error terms is Generalised Logistic. Some robust and nonparametric methods such as modified maximum likelihood (MML), least absolute deviations (LAD), Winsorized least squares, least trimmed squares (LTS), Theil and weighted Theil are compared via computer simulation. In order to evaluate the estimator performance, mean, variance, bias, mean square error (MSE) and relative mean square error (RMSE) are computed.

Subject Keywords

Probabilities.

URI

http://etd.lib.metu.edu.tr/upload/3/12605175/index.pdf
https://hdl.handle.net/11511/14278

Collections

Graduate School of Natural and Applied Sciences, Thesis