Regression Estimators Using Stratified Ranked Set Sampling

Arpita Chatterjee; Hani M. Samawi; Lili Yu; Daniel F. Linder; Jingxian Cai; Robert L. Vogel

Regression Estimators Using Stratified Ranked Set Sampling

Arpita Chatterjee, Hani M. Samawi, Lili Yu, Daniel F. Linder, Jingxian Cai, Robert L. Vogel

Georgia Southern University

Research output: Contribution to conference › Presentation

Abstract

This article is intended to investigate the performance of two types of stratified regression estimators, namely the separate and the combined estimator, using stratified ranked set sampling (SRSS), introduced by Samawi (1996). The expressions for mean and variance of the proposed estimates are derived and are shown to be unbiased. A simulation study is designed to compare the efficiency of SRSS relative to other sampling procedure under varying model scenarios. Our investigation indicates that the regression estimator of the population mean obtained through an SRSS becomes more efficient than the crude sample mean estimator using stratified simple random sampling. These findings are also illustrated with the help of a data set on bilirubin levels in babies in a neonatal intensive care unit.

Original language	American English
State	Published - Jun 16 2014
Event	International Chinese Statistical Association (ICSA) and the Korean International Statistical Society (KISS) Joint Applied Statistics Symposium - Duration: Jun 16 2014 → …

Conference

Conference	International Chinese Statistical Association (ICSA) and the Korean International Statistical Society (KISS) Joint Applied Statistics Symposium
Period	06/16/14 → …

Keywords

Ranked set sampling
Stratified ranked set sampling
Regression estimator

DC Disciplines

Biostatistics
Public Health

UN SDGs

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

Access to Document

http://www.statkiss.org/icsakiss2014/documents/abstractsBook.pdf

Cite this

@conference{716296a1ec884a2eb08edfac29975709,

title = "Regression Estimators Using Stratified Ranked Set Sampling",

abstract = " This article is intended to investigate the performance of two types of stratified regression estimators, namely the separate and the combined estimator, using stratified ranked set sampling (SRSS), introduced by Samawi (1996). The expressions for mean and variance of the proposed estimates are derived and are shown to be unbiased. A simulation study is designed to compare the efficiency of SRSS relative to other sampling procedure under varying model scenarios. Our investigation indicates that the regression estimator of the population mean obtained through an SRSS becomes more efficient than the crude sample mean estimator using stratified simple random sampling. These findings are also illustrated with the help of a data set on bilirubin levels in babies in a neonatal intensive care unit.",

keywords = "Ranked set sampling, Stratified ranked set sampling, Regression estimator",

author = "Arpita Chatterjee and Samawi, {Hani M.} and Lili Yu and Linder, {Daniel F.} and Jingxian Cai and Vogel, {Robert L.}",

year = "2014",

month = jun,

day = "16",

language = "American English",

note = "International Chinese Statistical Association (ICSA) and the Korean International Statistical Society (KISS) Joint Applied Statistics Symposium ; Conference date: 16-06-2014",

}

TY - CONF

T1 - Regression Estimators Using Stratified Ranked Set Sampling

AU - Chatterjee, Arpita

AU - Samawi, Hani M.

AU - Yu, Lili

AU - Linder, Daniel F.

AU - Cai, Jingxian

AU - Vogel, Robert L.

PY - 2014/6/16

Y1 - 2014/6/16

N2 - This article is intended to investigate the performance of two types of stratified regression estimators, namely the separate and the combined estimator, using stratified ranked set sampling (SRSS), introduced by Samawi (1996). The expressions for mean and variance of the proposed estimates are derived and are shown to be unbiased. A simulation study is designed to compare the efficiency of SRSS relative to other sampling procedure under varying model scenarios. Our investigation indicates that the regression estimator of the population mean obtained through an SRSS becomes more efficient than the crude sample mean estimator using stratified simple random sampling. These findings are also illustrated with the help of a data set on bilirubin levels in babies in a neonatal intensive care unit.

AB - This article is intended to investigate the performance of two types of stratified regression estimators, namely the separate and the combined estimator, using stratified ranked set sampling (SRSS), introduced by Samawi (1996). The expressions for mean and variance of the proposed estimates are derived and are shown to be unbiased. A simulation study is designed to compare the efficiency of SRSS relative to other sampling procedure under varying model scenarios. Our investigation indicates that the regression estimator of the population mean obtained through an SRSS becomes more efficient than the crude sample mean estimator using stratified simple random sampling. These findings are also illustrated with the help of a data set on bilirubin levels in babies in a neonatal intensive care unit.

KW - Ranked set sampling

KW - Stratified ranked set sampling

KW - Regression estimator

UR - https://digitalcommons.georgiasouthern.edu/biostat-facpres/23

UR - http://www.statkiss.org/icsakiss2014/documents/abstractsBook.pdf

M3 - Presentation

T2 - International Chinese Statistical Association (ICSA) and the Korean International Statistical Society (KISS) Joint Applied Statistics Symposium

Y2 - 16 June 2014

ER -

Regression Estimators Using Stratified Ranked Set Sampling

Abstract

Conference

Keywords

DC Disciplines

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this