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

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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.


American Public Health Association Annual Conference (APHA)


Chicago, IL