On Quantiles Estimation Based on Diﬀerent Stratified Sampling with Optimal Allocation

Hani M. Samawi; Jingjing Yin; Arpita Chatterjee; Haresh Rochani

On Quantiles Estimation Based on Diﬀerent Stratified Sampling with Optimal Allocation

Hani M. Samawi, Jingjing Yin, Arpita Chatterjee, Haresh Rochani

Georgia Southern University

Research output: Contribution to conference › Presentation

Abstract

In this work, we consider the problem of estimating a quantile function based on different stratified sampling mechanisms. First, we develop an estimate for population quantiles based on stratified simple random sampling (SSRS) and extend the discussion for stratified ranked set sampling (SRSS). We also study the asymptotic behavior of the proposed estimators. Here, we derive an analytical expression for the optimal allocation under both sampling schemes. Simulation studies are designed to examine the performance of the proposed estimators under varying distributional assumptions. The efficiency of the proposed estimates is further illustrated by analyzing a real data set containing TC biomarker values taken from 10,187 Chinese children and adults (>age 7) in the year 2009.

Original language	American English
State	Published - Mar 25 2018
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

Quantiles Estimation
Different Stratified
Optimal Allocation

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/spring2018/program/Abstracts.pdf

Cite this

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title = "On Quantiles Estimation Based on Diﬀerent Stratified Sampling with Optimal Allocation",

abstract = " In this work, we consider the problem of estimating a quantile function based on different stratified sampling mechanisms. First, we develop an estimate for population quantiles based on stratified simple random sampling (SSRS) and extend the discussion for stratified ranked set sampling (SRSS). We also study the asymptotic behavior of the proposed estimators. Here, we derive an analytical expression for the optimal allocation under both sampling schemes. Simulation studies are designed to examine the performance of the proposed estimators under varying distributional assumptions. The efficiency of the proposed estimates is further illustrated by analyzing a real data set containing TC biomarker values taken from 10,187 Chinese children and adults (>age 7) in the year 2009.",

keywords = "Quantiles Estimation, Different Stratified, Optimal Allocation",

author = "Samawi, {Hani M.} and Jingjing Yin and Arpita Chatterjee and Haresh Rochani",

year = "2018",

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day = "25",

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 - On Quantiles Estimation Based on Diﬀerent Stratified Sampling with Optimal Allocation

AU - Samawi, Hani M.

AU - Yin, Jingjing

AU - Chatterjee, Arpita

AU - Rochani, Haresh

PY - 2018/3/25

Y1 - 2018/3/25

N2 - In this work, we consider the problem of estimating a quantile function based on different stratified sampling mechanisms. First, we develop an estimate for population quantiles based on stratified simple random sampling (SSRS) and extend the discussion for stratified ranked set sampling (SRSS). We also study the asymptotic behavior of the proposed estimators. Here, we derive an analytical expression for the optimal allocation under both sampling schemes. Simulation studies are designed to examine the performance of the proposed estimators under varying distributional assumptions. The efficiency of the proposed estimates is further illustrated by analyzing a real data set containing TC biomarker values taken from 10,187 Chinese children and adults (>age 7) in the year 2009.

AB - In this work, we consider the problem of estimating a quantile function based on different stratified sampling mechanisms. First, we develop an estimate for population quantiles based on stratified simple random sampling (SSRS) and extend the discussion for stratified ranked set sampling (SRSS). We also study the asymptotic behavior of the proposed estimators. Here, we derive an analytical expression for the optimal allocation under both sampling schemes. Simulation studies are designed to examine the performance of the proposed estimators under varying distributional assumptions. The efficiency of the proposed estimates is further illustrated by analyzing a real data set containing TC biomarker values taken from 10,187 Chinese children and adults (>age 7) in the year 2009.

KW - Quantiles Estimation

KW - Different Stratified

KW - Optimal Allocation

UR - https://digitalcommons.georgiasouthern.edu/biostat-facpres/113

UR - https://www.enar.org/meetings/spring2018/program/Abstracts.pdf

M3 - Presentation

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

Y2 - 25 March 2018

ER -

On Quantiles Estimation Based on Diﬀerent Stratified Sampling with Optimal Allocation

Abstract

Conference

Keywords

DC Disciplines

UN SDGs

Access to Document

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Cite this