When interval-grouped data are available, the classical Parzen–Rosenblatt
kernel density estimator has to be modified to get a computable and useful approach
in this context. The new nonparametric grouped data estimator needs of the choice
of a smoothing parameter. In this paper, two different bandwidth selectors for this
estimator are analyzed. A plug-in bandwidth selector is proposed and its relative rate
of convergence obtained. Additionally, a bootstrap algorithm to select the bandwidth
in this framework is designed. This method is easy to implement and does not require
Monte Carlo. Both proposals are compared through simulations in different scenarios.
It is observed that when the sample size is medium or large and grouping is not heavy,
both bandwidth selection methods have a similar and good performance. However,
when the sample size is large and under heavy grouping scenarios, the bootstrap
bandwidth selector leads to better results.

EB00000004127K

Available

• Bandwidth selection in kernel density estimation for interval-grouped data