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.
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