.png){kind=icon}
返回
Analyzing AHP-matrices by regression
.png){kind=icon}
PRE
AI
DOI：10.1016/S0377-2217(02)00430-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the analytic hierarchy process (AHP) the decision maker makes comparisons between pairs of attributes or alternatives. In real applications the comparisons are subject to judgmental errors. Many AHP-matrices reported in the literature are found to be such that the logarithm of the comparison ratio can be sufficiently well modeled by a normal distribution with a constant variance. On the basis of this model we present the formulae for the evaluation of the standard deviations of the estimates of the AH P-weights obtained by regression analysis. In order to eliminate the effect of an outlier in the comparison ratios a robust regression technique is elaborated, and compared with the eigenvector method and the logarithmic least squares regression. A dissimilarity matrix approach is presented for the statistical simultaneous comparisons of the AHP-weights. The results are illustrated by simulation experiments. (C) 2002 Elsevier Science B.V. All rights reserved.
Keyword:
analytical hierarchy process
eigenvector method
regression
robust regression
multiple comparisons
simultaneous comparisons
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
2.2W
被引数:
6.4W
机构
暂无机构信息
引用论文
.png){kind=icon}
Learning Robustly Stabilizing Explicit Model Predictive Controllers: A Non-Regular Sampling Approach
OAAI
OAAI分享收藏
查看更多内容