Comparison between classical and robust estimation methods for regression model parameters in the case of incomplete data

Abstract

The problem of complete data is considered one of the most important problems that hinder statistical analysis. Therefore, this problem must be solved by finding sound solutions to it by using some methods that lead to accurate results or close to accuracy. The research aims to compare methods of statistical data analysis. Estimating incomplete data using the filling algorithms R-Estimators, L estimators, EM, and and the W method and the case of the normal distribution of data according to patterns, machines, and the method of losing these observations. A comparison was made between the robust methods and the maximum Likelihood method, and the efficiency of the above-mentioned methods was noted. The imitation method was used in addition to practice. the data on the function variables, which represent the demand for cash and its relationship with the gross domestic product, government consumer spending, and the consumer price index, were discussed for the period from 2000-2022.The process was to compare these methods, and the results were drawn to reach the experimental side , through the experimental and applied part, it is preferable to use robust methods over classical methods in the case of incomplete data.

Keywords

• r-estimators
• l estimators
• em
• w estimators
• incomplete data
• robust methods