On stratified bivariate ranked set sampling with optimal allocation for naïve and ratio estimators

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

doi:10.1080/02664763.2016.1177495

On stratified bivariate ranked set sampling with optimal allocation for naïve and ratio estimators

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

Georgia Southern University

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

The purpose of the current work is to introduce stratified bivariate ranked set sampling (SBVRSS) and investigate its performance for estimating the population mean using both naïve 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 are used to illustrate the procedure on a real data set. Based on our simulation, SBVRSS for ratio estimation is more efficient than using SBVSRS in all cases.

Original language	English
Pages (from-to)	457-473
Number of pages	17
Journal	Journal of Applied Statistics
Volume	44
Issue number	3
DOIs	https://doi.org/10.1080/02664763.2016.1177495
State	Published - Feb 17 2017

Keywords

Stratified bivariate ranked set sampling
naïve estimator
optimal allocation
ranked set sampling
ratio estimator

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10.1080/02664763.2016.1177495

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title = "On stratified bivariate ranked set sampling with optimal allocation for na{\"i}ve 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 mean using both na{\"i}ve 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 are used to illustrate the procedure on a real data set. Based on our simulation, SBVRSS for ratio estimation is more efficient than using SBVSRS in all cases.",

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AU - Vogel, Robert L.

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AB - The purpose of the current work is to introduce stratified bivariate ranked set sampling (SBVRSS) and investigate its performance for estimating the population mean using both naïve 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 are used to illustrate the procedure on a real data set. Based on our simulation, SBVRSS for ratio estimation is more efficient than using SBVSRS in all cases.

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On stratified bivariate ranked set sampling with optimal allocation for naïve and ratio estimators

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Keywords

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