The Use of the Rank Transform in Regression
The Use of the Rank Transform in Regression
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DOI：
10.1080/00401706.1979.10489820
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发表时间：
1979-11
期刊：
影响因子：
2.5
通讯作者：
R. Iman;W. Conover
中科院分区：
文献类型：
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作者：
R. Iman;W. Conover
排名变换是一个简单的过程，涉及用相应的排名替换数据。作者之前已经证明，秩变换在实验设计的假设检验中非常有用。本研究展示了在回归中使用排序变换的结果。 Daniel 和 Wood [8] 给出的两组数据被考虑用于说明简单回归和多元回归中的秩变换。还给出了蒙特卡罗研究的结果，该研究将等级回归与一些已发表的等渗回归蒙特卡罗结果进行了比较。这项蒙特卡罗研究也经过修改，以将排名回归与稳健回归进行比较。另一个例子给出了通过排名回归对大型计算机代码进行分析的结果。等级变换是一个简单、可重复的过程，与 Andrews [1] 给出的其他方法相比具有优势。我们的研究表明该方法在单调数据上效果很好。
The rank transform is a simple procedure which involves replacing the data with their corresponding ranks. The rank transform has previously been shown by the authors to be useful in hypothesis testing with respect to experimental designs. This study shows the results of using the rank transform in regression. Two sets of data given by Daniel and Wood [8] are considered for purposes of illustrating the rank transform in simple and multiple regression. Also given are the results of a Monte Carlo study which compares regression on ranks with some published Monte Carlo results on isotonic regression. This Monte Carlo study is also modified to compare regression on ranks with robust regression. Another illustration gives the results of analyses on large computer codes by regression on ranks. The rank transform is a simple, repeatable process that compares favorably with other methods such as given by Andrews [1]. Our studies indicate the method works quite well on monotonic data.