Semi Parametric Estimators for Quantile Model Via LASSO and SCAD with Missing Data

Authors

• Aws Adnan Al-Tai, Department of Statistics, College of Administration & Economics, University of Baghdad, Republic of Iraq
• Qutaiba N. Nayef Al-Qazaz, Department of Statistics, College of Administration & Economics, University of Baghdad, Republic of Iraq

Abstract

In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method

DOI

10.33095/jeas.v28i133.2351

Subject Area

Statistical

First Page

82

Last Page

96

Rights

http://creativecommons.org/licenses/by-nc-nd/4.0

Recommended Citation

Al-Tai, A. A., & Al-Qazaz, Q. N. (2022). Semi Parametric Estimators for Quantile Model Via LASSO and SCAD with Missing Data. Journal of Economics and Administrative Sciences, 28(133), 82-96. https://doi.org/10.33095/jeas.v28i133.2351