On Stratified Bivariate Ranked Set Sampling with Optimal Allocation for Naive and Ratio Estimators

Lili Yu; Hani M. Samawi; Daniel F. Linder; Arpita Chatterjee; Yisong Huang; Robert L. Vogel

On Stratified Bivariate Ranked Set Sampling with Optimal Allocation for Naive and Ratio Estimators

Lili Yu, Hani M. Samawi, Daniel F. Linder, Arpita Chatterjee, Yisong Huang, Robert L. Vogel

Georgia Southern University

Research output: Contribution to conference › Presentation

Abstract

The purpose of the current work is to introduce stratified bivariate ranked set sampling (SBVRSS) and investigate its performance for estimating the population means using naive and ratio methods. The properties of the proposed estimator are derived along with the optimal allocation with respect to stratification. We conduct a simulation study to demonstrate the relative efficiency of SBVRSS as compared to stratified bivariate simple random sampling (SBVSRS) for ratio estimation. Data that consist of weights and bilirubin levels in the blood of 120 babies used to illustrate the procedure on a real data set, with our results indicating that SBVRSS for ratio estimation is more efficient than using SBVSRS in all cases presented in the simulations

Original language	American English
State	Published - Mar 1 2015
Event	Eastern North American Region International Biometric Society Annual Conference (ENAR) - Duration: Mar 15 2015 → …

Conference

Conference	Eastern North American Region International Biometric Society Annual Conference (ENAR)
Period	03/15/15 → …

Disciplines

Biostatistics
Public Health

Keywords

Optimal allocation
Naive and ratio estimators
Stratified bivariate ranked set sampling
SBVRSS

UN SDGs

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

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https://www.enar.org/meetings/past.cfm

Cite this

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title = "On Stratified Bivariate Ranked Set Sampling with Optimal Allocation for Naive and Ratio Estimators",

abstract = " The purpose of the current work is to introduce stratified bivariate ranked set sampling (SBVRSS) and investigate its performance for estimating the population means using naive and ratio methods. The properties of the proposed estimator are derived along with the optimal allocation with respect to stratification. We conduct a simulation study to demonstrate the relative efficiency of SBVRSS as compared to stratified bivariate simple random sampling (SBVSRS) for ratio estimation. Data that consist of weights and bilirubin levels in the blood of 120 babies used to illustrate the procedure on a real data set, with our results indicating that SBVRSS for ratio estimation is more efficient than using SBVSRS in all cases presented in the simulations",

keywords = "Optimal allocation, Naive and ratio estimators, Stratified bivariate ranked set sampling, SBVRSS",

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

year = "2015",

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day = "1",

language = "American English",

note = "Eastern North American Region International Biometric Society Annual Conference (ENAR) ; Conference date: 15-03-2015",

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TY - CONF

T1 - On Stratified Bivariate Ranked Set Sampling with Optimal Allocation for Naive and Ratio Estimators

AU - Yu, Lili

AU - Samawi, Hani M.

AU - Linder, Daniel F.

AU - Chatterjee, Arpita

AU - Huang, Yisong

AU - Vogel, Robert L.

PY - 2015/3/1

Y1 - 2015/3/1

N2 - The purpose of the current work is to introduce stratified bivariate ranked set sampling (SBVRSS) and investigate its performance for estimating the population means using naive and ratio methods. The properties of the proposed estimator are derived along with the optimal allocation with respect to stratification. We conduct a simulation study to demonstrate the relative efficiency of SBVRSS as compared to stratified bivariate simple random sampling (SBVSRS) for ratio estimation. Data that consist of weights and bilirubin levels in the blood of 120 babies used to illustrate the procedure on a real data set, with our results indicating that SBVRSS for ratio estimation is more efficient than using SBVSRS in all cases presented in the simulations

AB - The purpose of the current work is to introduce stratified bivariate ranked set sampling (SBVRSS) and investigate its performance for estimating the population means using naive and ratio methods. The properties of the proposed estimator are derived along with the optimal allocation with respect to stratification. We conduct a simulation study to demonstrate the relative efficiency of SBVRSS as compared to stratified bivariate simple random sampling (SBVSRS) for ratio estimation. Data that consist of weights and bilirubin levels in the blood of 120 babies used to illustrate the procedure on a real data set, with our results indicating that SBVRSS for ratio estimation is more efficient than using SBVSRS in all cases presented in the simulations

KW - Optimal allocation

KW - Naive and ratio estimators

KW - Stratified bivariate ranked set sampling

KW - SBVRSS

UR - https://digitalcommons.georgiasouthern.edu/biostat-facpres/19

UR - https://www.enar.org/meetings/past.cfm

M3 - Presentation

T2 - Eastern North American Region International Biometric Society Annual Conference (ENAR)

Y2 - 15 March 2015

ER -

On Stratified Bivariate Ranked Set Sampling with Optimal Allocation for Naive and Ratio Estimators

Abstract

Conference

Disciplines

Keywords

UN SDGs

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