Convergence Rate for Estimator of Distribution Function under NSD Assumption with an Application

Abstract

In this paper, the kernel distribution function estimator for negative superadditive dependent (NSD) random variables is studied. The exponential inequalities and exponential rate for the kernel estimator are investigated. Under certain regularity conditions, the optimal bandwidth is determined using the mean squared error and is found to be the same as that in the independent identically distributed case. A simulation study to examine the behavior of the kernel and empirical estimators is given. Moreover, a real data set in hydrology is analyzed to demonstrate the structure of negative superadditive dependence, and as a result, the kernel distribution function estimator of the data is investigated.