Kernel Density Estimation Based on Progressive Type-II Censoring

Hani Samawi; Amal Helu; Haresh Rochani; Jingjing Yin; Robert L. Vogel

Kernel Density Estimation Based on Progressive Type-II Censoring

Hani Samawi, Amal Helu, Haresh Rochani, Jingjing Yin, Robert L. Vogel

Carnegie Mellon University

Research output: Contribution to conference › Presentation

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 → …

Keywords

Kernel Density
Type-II

DC Disciplines

Biostatistics
Public Health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

https://www.enar.org/meetings/spring2017/program/Abstracts.pdf

Cite this

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title = "Kernel Density Estimation Based on Progressive Type-II Censoring",

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.",

keywords = "Kernel Density, Type-II",

author = "Hani Samawi and Amal Helu and Haresh Rochani and Jingjing Yin and Vogel, {Robert L.}",

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day = "13",

language = "American English",

note = "Eastern North American Region International Biometric Society Spring Meeting (ENAR) ; Conference date: 25-03-2018",

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TY - CONF

T1 - Kernel Density Estimation Based on Progressive Type-II Censoring

AU - Samawi, Hani

AU - Helu, Amal

AU - Rochani, Haresh

AU - Yin, Jingjing

AU - Vogel, Robert L.

PY - 2017/3/13

Y1 - 2017/3/13

N2 - 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.

AB - 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.

KW - Kernel Density

KW - Type-II

UR - https://digitalcommons.georgiasouthern.edu/biostat-facpres/127

UR - https://www.enar.org/meetings/spring2017/program/Abstracts.pdf

M3 - Presentation

T2 - Eastern North American Region International Biometric Society Spring Meeting (ENAR)

Y2 - 25 March 2018

ER -

Kernel Density Estimation Based on Progressive Type-II Censoring

Abstract

Conference

Keywords

DC Disciplines

UN SDGs

Access to Document

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Cite this