Abstract:
The precision and accuracy of any estimation can inform one whether to use or not to use the estimated values. It is the crux of
the matter to many if not all statisticians. For this to be realized biases of the estimates are normally checked and eliminated or
at least minimized. Even with this in mind getting a model that fits the data well can be a challenge. There are many situations
where parametric estimation is disadvantageous because of the possible misspecification of the model. Under such circumstance,
many researchers normally allow the data to suggest a model for itself in the technique that has become so popular in recent years
called the nonparametric regression estimation. In this technique the use of kernel estimators is common. This paper explores
the famous Nadaraya–Watson estimator and local linear regression estimator on the boundary bias. A global measure of error
criterion-asymptotic mean integrated square error (AMISE) has been computed from simulated data at the empirical stage to
assess the performance of the two estimators in regression estimation. This study shows that local linear regression estimator
has a sterling performance over the standard Nadaraya–Watson estimator.
