Statistical inference with empty strata in judgment post stratified samples

Statistical inference with empty strata in judgment post stratified samples This article develops estimators for certain population characteristics using a judgment post stratified (JPS) sample. The paper first constructs a conditional JPS sample with a reduced set size K by conditioning on the ranks of the measured observations of the original JPS sample of set size $$H \ge K$$ H ≥ K . The paper shows that the estimators of the population mean, median and distribution function based on this conditional JPS sample are consistent and have limiting normal distributions. It is shown that the proposed estimators, unlike the ratio and regression estimators, where they require a strong linearity assumption, only need a monotonic relationship between the response and auxiliary variable. For moderate sample sizes, the paper provides a bootstrap distribution to draw statistical inference. A small-scale simulation study shows that the proposed estimators based on a reduced set JPS sample perform better than the corresponding estimators based on a regular JPS sample. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of the Institute of Statistical Mathematics Springer Journals

Statistical inference with empty strata in judgment post stratified samples

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Publisher
Springer Japan
Copyright
Copyright © 2016 by The Institute of Statistical Mathematics, Tokyo
Subject
Statistics; Statistics, general; Statistics for Business/Economics/Mathematical Finance/Insurance
ISSN
0020-3157
eISSN
1572-9052
D.O.I.
10.1007/s10463-016-0572-y
Publisher site
See Article on Publisher Site

Abstract

This article develops estimators for certain population characteristics using a judgment post stratified (JPS) sample. The paper first constructs a conditional JPS sample with a reduced set size K by conditioning on the ranks of the measured observations of the original JPS sample of set size $$H \ge K$$ H ≥ K . The paper shows that the estimators of the population mean, median and distribution function based on this conditional JPS sample are consistent and have limiting normal distributions. It is shown that the proposed estimators, unlike the ratio and regression estimators, where they require a strong linearity assumption, only need a monotonic relationship between the response and auxiliary variable. For moderate sample sizes, the paper provides a bootstrap distribution to draw statistical inference. A small-scale simulation study shows that the proposed estimators based on a reduced set JPS sample perform better than the corresponding estimators based on a regular JPS sample.

Journal

Annals of the Institute of Statistical MathematicsSpringer Journals

Published: Jul 11, 2016

References

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