Kernel Density Estimation Based on Progressive Type-II Censoring

Presenters/Authors

Hani Samawi, Georgia Southern UniversityFollow
Amal Helu, Carnegie Mellon UniversityFollow
Haresh Rochani, Georgia Southern UniversityFollow
Jingjing Yin, Georgia Southern UniversityFollow
Robert L. Vogel, Georgia Southern UniversityFollow

Document Type

Presentation

Presentation Date

3-13-2017

Abstract or Description

Progressive censoring is essential for researchers in industry as a mean to remove subjects before the final termination point. Recently, kernel density estimation has been intensively investigated due to its nice properties and applications. In this paper we investigate the asymptotic properties of the kernel density estimators based on progressive type-II censoring and their application to hazard function estimation. A bias adjusted kernel density estimator is also suggested. Our simulation indicates that the kernel density estimates under progressive type-II censoring is competitive with kernel density estimates under simple random sampling.

Sponsorship/Conference/Institution

Eastern North American Region International Biometric Society Spring Meeting (ENAR)

Location

Washington, DC

Recommended Citation

Samawi, Hani, Amal Helu, Haresh Rochani, Jingjing Yin, Robert L. Vogel. 2017. "Kernel Density Estimation Based on Progressive Type-II Censoring." Biostatistics Faculty Presentations. Presentation 127.
https://digitalcommons.georgiasouthern.edu/biostat-facpres/127