Cautionary note on unbalanced ranked-set sampling.

Balanced ranked-set sampling (RSS) offers improved statistical inference in situations where the units to be sampled can be ranked relative to each other prior to formal measurement. Recent work has shown that provided the ranking process is perfect, unbalanced RSS can do even better. In this articl...

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Main Authors: Husby, Chad E., Stasny, Elizabeth A., Wolfe, Douglas A., Frey, Jesse.
Format: Villanova Faculty Authorship
Language:English
Published: 2006
Online Access:http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:176358
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spelling Cautionary note on unbalanced ranked-set sampling.
Husby, Chad E.
Stasny, Elizabeth A.
Wolfe, Douglas A.
Frey, Jesse.
Balanced ranked-set sampling (RSS) offers improved statistical inference in situations where the units to be sampled can be ranked relative to each other prior to formal measurement. Recent work has shown that provided the ranking process is perfect, unbalanced RSS can do even better. In this article, we examine the performance of one unbalanced RSS technique when the ranking process is not perfect. Using an Ohio corn production data set, we show that median-based unbalanced RSS outperforms balanced RSS in estimating a population median if the rankings are nearly perfect.We also show, however, that median-based unbalanced RSS may perform extremely poorly when the ranking process is less than perfect. This effect is particularly pronounced when the variable of interest has a skewed distribution.We thus offer a note of caution for users of unbalanced RSS.
2006-04-08
Villanova Faculty Authorship
vudl:176358
Journal of Statistical Computation and Simulation 77(10), October 2007, 869-878.
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dc.title_txt_mv Cautionary note on unbalanced ranked-set sampling.
dc.creator_txt_mv Husby, Chad E.
Stasny, Elizabeth A.
Wolfe, Douglas A.
Frey, Jesse.
dc.description_txt_mv Balanced ranked-set sampling (RSS) offers improved statistical inference in situations where the units to be sampled can be ranked relative to each other prior to formal measurement. Recent work has shown that provided the ranking process is perfect, unbalanced RSS can do even better. In this article, we examine the performance of one unbalanced RSS technique when the ranking process is not perfect. Using an Ohio corn production data set, we show that median-based unbalanced RSS outperforms balanced RSS in estimating a population median if the rankings are nearly perfect.We also show, however, that median-based unbalanced RSS may perform extremely poorly when the ranking process is less than perfect. This effect is particularly pronounced when the variable of interest has a skewed distribution.We thus offer a note of caution for users of unbalanced RSS.
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author Husby, Chad E.
Stasny, Elizabeth A.
Wolfe, Douglas A.
Frey, Jesse.
spellingShingle Husby, Chad E.
Stasny, Elizabeth A.
Wolfe, Douglas A.
Frey, Jesse.
Cautionary note on unbalanced ranked-set sampling.
author_facet Husby, Chad E.
Stasny, Elizabeth A.
Wolfe, Douglas A.
Frey, Jesse.
dc_source_str_mv Journal of Statistical Computation and Simulation 77(10), October 2007, 869-878.
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dc_date_str 2006-04-08
dc_title_str Cautionary note on unbalanced ranked-set sampling.
description Balanced ranked-set sampling (RSS) offers improved statistical inference in situations where the units to be sampled can be ranked relative to each other prior to formal measurement. Recent work has shown that provided the ranking process is perfect, unbalanced RSS can do even better. In this article, we examine the performance of one unbalanced RSS technique when the ranking process is not perfect. Using an Ohio corn production data set, we show that median-based unbalanced RSS outperforms balanced RSS in estimating a population median if the rankings are nearly perfect.We also show, however, that median-based unbalanced RSS may perform extremely poorly when the ranking process is less than perfect. This effect is particularly pronounced when the variable of interest has a skewed distribution.We thus offer a note of caution for users of unbalanced RSS.
title Cautionary note on unbalanced ranked-set sampling.
title_full Cautionary note on unbalanced ranked-set sampling.
title_fullStr Cautionary note on unbalanced ranked-set sampling.
title_full_unstemmed Cautionary note on unbalanced ranked-set sampling.
title_short Cautionary note on unbalanced ranked-set sampling.
title_sort cautionary note on unbalanced ranked-set sampling.
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