# Multiple Observers Ranked Set Samples for Shrinkage Estimators

arXiv: https://arxiv.org/abs/2110.07851 · HTML: https://mlsanity.com/paper/2110.07851/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2021.

## Built on by (most cited first, 1 of 1)

- [Liu-type Shrinkage Estimators for Mixture of Poisson Regressions with Experts: A Heart Disease Study](https://mlsanity.com/paper/2309.05838/) (2023)

## Abstract (arXiv)

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.