A Comparative Study of Nonparametric Kernel estimators with Gaussian Weight Function

Abstract

Nowadays, Parametric methods become unfavorable by researchers because of the restrictions on using them and losing the flexibility in estimating and analysis the data. Therefore, the researchers preferred the nonparametric method which proved their efficiency and capable to analysis the data without of predetermined assumptions. Consequently, the data and their included information are becoming who determine the functional shape for the studied population and there are no parameters instead of the observations. The objective of estimating the nonparametric regression function is to approximate the regression function to the real regression function. On the other hand, COVID-19 pandemic nowadays speared in all the countries one of them is Iraq. The function of infection speared have been studied in different countries but not in Iraq. Therefore, the aim of our research is to apply three nonparametric Kernel estimators with Gaussian weighted function to model and forecast the number of infections of COVID-19 in Iraq. R software and the data represent the daily number of COVID-19 infections for the period 23/2/2020 to 21/6/2020 are used to apply many models and choose the appropriate one. The results of applying three nonparametric Kernel model that the Priestley-Chao model is the appropriate one in all the sample sizes and other conditions