Multiple Observers Ranked Set Samples for Shrinkage Estimators
release_elqnkmzxundxhpuzmemtacq44a
by
Andrew David Pearce, Armin Hatefi
2021
Abstract
Ranked set sampling (RSS) is used as a powerful data collection technique for
situations where measuring the study variable requires a costly and/or tedious
process while the sampling units can be ranked easily (e.g., osteoporosis
research). In this paper, we develop ridge and Liu-type shrinkage estimators
under RSS data from multiple observers to handle the collinearity problem in
estimating coefficients of linear regression, stochastic restricted regression
and logistic regression. Through extensive numerical studies, we show that
shrinkage methods with the multi-observer RSS result in more efficient
coefficient estimates. The developed methods are finally applied to bone
mineral data for analysis of bone disorder status of women aged 50 and older.
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