A Comparison of Single Imputation Methods for Missing Values in Nonparametric Regression Models

Authors

• Qutaiba N. Nayef Al-Qazaz, Department of Statistics, College of Administration & Economics, University of Baghdad, Republic of Iraq
• Munaf Yousif Hmood, Department of Statistics, College of Administration and Economics, University of Baghdad, Iraq

Abstract

In this paper, we will study non parametric model when the response variable have missing data (non response) in observations it under missing mechanisms MCAR, then we suggest Kernel-Based Non-Parametric Single-Imputation instead of missing value and compare it with Nearest Neighbor Imputation by using the simulation about some difference models and with difference cases as the sample size, variance and rate of missing data.

DOI

10.33095/jeas.v15i53.1209

Subject Area

Statistical

First Page

223

Last Page

235

Rights

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

Recommended Citation

Al-Qazaz, Q. N., & Hmood, M. Y. (2022). A Comparison of Single Imputation Methods for Missing Values in Nonparametric Regression Models. Journal of Economics and Administrative Sciences, 15(53), 223-235. https://doi.org/10.33095/jeas.v15i53.1209