On kernel density estimation based on different stratified sampling with optimal allocation

Hani Samawi, Arpita Chatterjee, JingJing Yin and Haresh Rochani

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 22, 10973-10990

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:46:y:2017:i:22:p:10973-10990

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DOI: 10.1080/03610926.2016.1257714

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