Abstract
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.
| Original language | American English |
|---|---|
| State | Published - Mar 13 2017 |
| Event | Eastern North American Region International Biometric Society Spring Meeting (ENAR) - Duration: Mar 25 2018 → … |
Conference
| Conference | Eastern North American Region International Biometric Society Spring Meeting (ENAR) |
|---|---|
| Period | 03/25/18 → … |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Disciplines
- Biostatistics
- Public Health
Keywords
- Kernel Density
- Type-II
Fingerprint
Dive into the research topics of 'Kernel Density Estimation Based on Progressive Type-II Censoring'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver