A Monte Carlo study of traditional and rank-based picked-points analyses

作者：Watcharotone Kuanwong; McKean Joseph W^*; Huitema Bradley E

来源：Communications in Statistics - Simulation and Computation, 2017, 46(5): 4050-4066.

DOI：10.1080/03610918.2015.1091075

• ANCOVA
• Heterogeneous slopes
• High breakdown estimates
• Nonparametrics
• Rank-based analyses
• Skewed distributions
• Wilcoxon procedures
• Primary 62G35
• Secondary 62G05
• 62J05

In analysis of covariance with heteroscedastic slopes a picked-points analysis is often performed. Least-squares based picked-points analyses often lose efficiency (at times substantial) for nonnormal error distributions. Robust rank-based picked-points analyses are developed which are optimizable for heavy-tailed and/or skewed error distributions. The results of a Monte Carlo investigation of these analyses are presented. The situations include the normal model and models which violate it in one or several ways. Empirically the rank-based analyses appear to be valid over all these situations and more powerful than the least squares analysis for all the nonnormal models, while losing little efficiency at the normal model.

• 出版日期2017