The article looks at how to estimate the error distribution in nonparametric regression when some data is missing. They use a method called local polynomial smoothing to estimate the regression function. The researchers found that using only complete cases to estimate the error distribution is an efficient method. They also discovered a way to estimate different aspects of the error distribution accurately. Additionally, they showed that this method follows a specific statistical pattern. Finally, they tested their method in a simulation study to see how well it works.