A Comparison of Two Rank-Based Methods for the Analysis of Linear Models
作者:
JosephW. McKean,
ThomasJ. Vidmar,
期刊:
The American Statistician
(Taylor Available online 1994)
卷期:
Volume 48,
issue 3
页码: 220-229
ISSN:0003-1305
年代: 1994
DOI:10.1080/00031305.1994.10476061
出版商: Taylor & Francis Group
关键词: Analysis of covariance;Factorial designs;Nonparametric;Restimates;Ranks;Rank transform;Robust
数据来源: Taylor
摘要:
Two rank-based methods for analyzing linear models are compared. A robust general linear model (RGLM); similar to least squares, offers a complete analysis of a model, including estimation, testing, and diagnostic checks. It is supported by asymptotic theory and is highly efficient. The other is the rank transform (RT), which offers a testing procedure. Unlike RGLM, the RT is not supported by a general theory, and although initial simulation studies appeared promising, recent theoretical and Monte Carlo studies question the wisdom of doing RT's on designs as simple as two-way models. The two analyses are contrasted over factorial designs on which they can substantially differ. These differences are highlighted in a simulation study on a three-way factorial design. We conclude the contrast with an analysis of covariance example.
点击下载:
PDF (1075KB)
返 回