Unbalancedness under RSS-structured Cluster Randomized Designs
RSS 구조화된 무작위 군집 설계에서의 불균형성
- 임요한; Xinlei Wang
- 자연과학대학 통계학과
- Issue Date
- 서울대학교 대학원
- Hierarchical linear models ; missing data ; Neyman allocation ; nonparametric inference ; relative efficiency ; treatment effect ; unbalanced ranked set sampling
- 학위논문 (박사)-- 서울대학교 대학원 : 통계학과, 2016. 2. Xinlei Wang.
- In this thesis, we consider unbalancedness under cluster randomized designs (CRDs) using ranked set sampling (RSS), and extend nonparametric inference procedures, previously developed by Wang et al. (2016) for the use of balanced RSS (BRSS) with CRDs, to account for the unbalanced structures. The unbalanced structures are from (i) the use of unbalanced ranked set sampling (URSS) with CRDs or (ii) the use of BRSS with CRDs having unbalancedly sized groups. First, we consider URSS-structured CRDs. We theoretically study the URSS estimators of the treatment effect and numerically compare the relative effciency of the URSS vs. BRSS estimators. We also study and compare the power of the URSS tests vs. their BRSS counterparts via simulation. Second, we consider BRSS-structured CRDs having unbalancedly sized groups. We modify and improve the BRSS tests in Wang et al. (2016) and compare power of the modified BRSS tests vs. their original BRSS counterparts via simulation. Further, we illustrate each inference procedure with a data example using an educational data. Finally, we offer flexibility of incorporating RSS in CRDs in practice.