On Kernel Density Estimation Based on Different Stratified Sampling With Optimal Allocation

Authors

Hani M. Samawi, Georgia Southern UniversityFollow
Arpita Chatterjee, Georgia Southern UniversityFollow
Jingjing Yin, Georgia Southern UniversityFollow
Haresh Rochani, Georgia Southern UniversityFollow

Document Type

Article

Publication Date

8-7-2017

Publication Title

Communications in Statistics - Theory and Methods

DOI

10.1080/03610926.2016.1257714

ISSN

1532-415X

Abstract

Kernel density estimation is probably the most widely used non parametric statistical method for estimating probability densities. In this paper, we investigate the performance of kernel density estimator based on stratified simple and ranked set sampling. Some asymptotic properties of kernel estimator are established under both sampling schemes. Simulation studies are designed to examine the performance of the proposed estimators under varying distributional assumptions. These findings are also illustrated with the help of a dataset on bilirubin levels in babies in a neonatal intensive care unit.

Recommended Citation

Samawi, Hani M., Arpita Chatterjee, Jingjing Yin, Haresh Rochani. 2017. "On Kernel Density Estimation Based on Different Stratified Sampling With Optimal Allocation." Communications in Statistics - Theory and Methods, 46 (22): 10973-10990. doi: 10.1080/03610926.2016.1257714
https://digitalcommons.georgiasouthern.edu/biostat-facpubs/148