Biostatistics: Faculty Presentations (2003-2018)
Kernel Density Estimation Based on Progressive Type-II Censoring
Document Type
Presentation
Presentation Date
3-13-2017
Copyright
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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 (2003-2018).
Presentation 127.
https://digitalcommons.georgiasouthern.edu/biostat-facpres/127
