Biostatistics: Faculty Publications
On Kernel Density Estimation Based on Different Stratified Sampling With Optimal Allocation
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
Copyright
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