More Efficient Logistic Analysis Using Moving Extreme Ranked Set Sampling: Application to Children Obesity

Hani M. Samawi; Haresh Rochani; Daniel Linder; Jingjing Yin; Arpita Chatterjee; Lili Yu; Robert L. Vogel

More Efficient Logistic Analysis Using Moving Extreme Ranked Set Sampling: Application to Children Obesity

Hani M. Samawi, Haresh Rochani, Daniel Linder, Jingjing Yin, Arpita Chatterjee, Lili Yu, Robert L. Vogel

Georgia Southern University

Research output: Contribution to conference › Presentation

Abstract

Logistic regression is widely used for analyzing dichotomous responses, has been intensively applied in social, medical, behavioral and public health sciences research. The paper provides a more efficient logistic regression analysis of a set of predictors based on subjects selected using Moving Extreme Ranked Set Sampling (MERSS) scheme by ranking on one of the available axillary variable know to be associated with the variable of interest (response variable). The paper shows that this approach will provide more powerful testing procedure as well as more efficient odds ratio and parameter estimation than using simple random sample (SRS). Theoretical derivation and simulation studies will be provided. Real data from children health survey are used to illustrate the procedures developed in this paper.

Original language	American English
State	Published - Nov 2 2015
Event	American Public Health Association Annual Meeting (APHA) - Duration: Nov 7 2017 → …

Conference

Conference	American Public Health Association Annual Meeting (APHA)
Period	11/7/17 → …

Keywords

Child mental health
Adolescent mental health
Statistics
Obesity

DC Disciplines

Biostatistics
Public Health

UN SDGs

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

Access to Document

https://apha.confex.com/apha/143am/webprogram/Paper317008.html

Cite this

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title = "More Efficient Logistic Analysis Using Moving Extreme Ranked Set Sampling: Application to Children Obesity",

abstract = " Logistic regression is widely used for analyzing dichotomous responses, has been intensively applied in social, medical, behavioral and public health sciences research. The paper provides a more efficient logistic regression analysis of a set of predictors based on subjects selected using Moving Extreme Ranked Set Sampling (MERSS) scheme by ranking on one of the available axillary variable know to be associated with the variable of interest (response variable). The paper shows that this approach will provide more powerful testing procedure as well as more efficient odds ratio and parameter estimation than using simple random sample (SRS). Theoretical derivation and simulation studies will be provided. Real data from children health survey are used to illustrate the procedures developed in this paper.",

keywords = "Child mental health, Adolescent mental health, Statistics, Obesity",

author = "Samawi, {Hani M.} and Haresh Rochani and Daniel Linder and Jingjing Yin and Arpita Chatterjee and Lili Yu and Vogel, {Robert L.}",

year = "2015",

month = nov,

day = "2",

language = "American English",

note = "American Public Health Association Annual Meeting (APHA) ; Conference date: 07-11-2017",

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TY - CONF

T1 - More Efficient Logistic Analysis Using Moving Extreme Ranked Set Sampling: Application to Children Obesity

AU - Samawi, Hani M.

AU - Rochani, Haresh

AU - Linder, Daniel

AU - Yin, Jingjing

AU - Chatterjee, Arpita

AU - Yu, Lili

AU - Vogel, Robert L.

PY - 2015/11/2

Y1 - 2015/11/2

N2 - Logistic regression is widely used for analyzing dichotomous responses, has been intensively applied in social, medical, behavioral and public health sciences research. The paper provides a more efficient logistic regression analysis of a set of predictors based on subjects selected using Moving Extreme Ranked Set Sampling (MERSS) scheme by ranking on one of the available axillary variable know to be associated with the variable of interest (response variable). The paper shows that this approach will provide more powerful testing procedure as well as more efficient odds ratio and parameter estimation than using simple random sample (SRS). Theoretical derivation and simulation studies will be provided. Real data from children health survey are used to illustrate the procedures developed in this paper.

AB - Logistic regression is widely used for analyzing dichotomous responses, has been intensively applied in social, medical, behavioral and public health sciences research. The paper provides a more efficient logistic regression analysis of a set of predictors based on subjects selected using Moving Extreme Ranked Set Sampling (MERSS) scheme by ranking on one of the available axillary variable know to be associated with the variable of interest (response variable). The paper shows that this approach will provide more powerful testing procedure as well as more efficient odds ratio and parameter estimation than using simple random sample (SRS). Theoretical derivation and simulation studies will be provided. Real data from children health survey are used to illustrate the procedures developed in this paper.

KW - Child mental health

KW - Adolescent mental health

KW - Statistics

KW - Obesity

UR - https://digitalcommons.georgiasouthern.edu/biostat-facpres/8

UR - https://apha.confex.com/apha/143am/webprogram/Paper317008.html

M3 - Presentation

T2 - American Public Health Association Annual Meeting (APHA)

Y2 - 7 November 2017

ER -

More Efficient Logistic Analysis Using Moving Extreme Ranked Set Sampling: Application to Children Obesity

Abstract

Conference

Keywords

DC Disciplines

UN SDGs

Access to Document

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