Proposed method to estimate missing values in Non - Parametric multiple regression model
DOI:
https://doi.org/10.33095/jeas.v22i89.626Keywords:
الانحدار المتعدد اللامعلمي- المشاهدات المفقودة- آلية الفقدان- نمط الفقدان- مقدر Nadary – Watson - المربعات الصغرى للعبور الشرعي, Non-Parametric Multiple Regression Model- missing observation, Missing Data Mechanisms- Patterns of Missing Data- Nadaraya – Watson Estimator- Least Squared Cross ValidationAbstract
In this paper, we will provide a proposed method to estimate missing values for the Explanatory variables for Non-Parametric Multiple Regression Model and compare it with the Imputation Arithmetic mean Method, The basis of the idea of this method was based on how to employ the causal relationship between the variables in finding an efficient estimate of the missing value, we rely on the use of the Kernel estimate by Nadaraya – Watson Estimator , and on Least Squared Cross Validation (LSCV) to estimate the Bandwidth, and we use the simulation study to compare between the two methods.
Downloads
Published
Issue
Section
License
Articles submitted to the journal should not have been published before in their current or substantially similar form, or be under consideration for publication with another journal. Please see JEAS originality guidelines for details. Use this in conjunction with the points below about references, before submission i.e. always attribute clearly using either indented text or quote marks as well as making use of the preferred Harvard style of formatting. Authors submitting articles for publication warrant that the work is not an infringement of any existing copyright and will indemnify the publisher against any breach of such warranty. For ease of dissemination and to ensure proper policing of use, papers and contributions become the legal copyright of the publisher unless otherwise agreed.
The editor may make use of Turnitin software for checking the originality of submissions received.
How to Cite
How to use the OJS system 
