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A statistical comparison of three goodness-of-fit criteria used inmodelling distances

机译：在距离建模中使用的三个拟合优度标准的统计比较

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摘要

Distance predicting functions may be used in a variety of applications for estimating travel distances between points. To evaluate the accuracy of a distance predicting function and to determine its parameters, a goodness-of-fit criteria is employed. AD (Absolute Deviations), SD (Squared Deviations) and NAD (Normalized Absolute Deviations) are the three criteria that are mostly employed in practice. In the literature some assumptions have been made about the properties of each criterion. In this paper, we present statistical analyses performed to compare the three criteria from different perspectives. For this purpose, we employ the?kpθ-norm as the distance predicting function, and statistically compare the three criteria by using normalized absolute prediction error distributions in seventeen geographical regions. We find that there exist no significant differences between the criteria. However, since the criterion SD has desirable properties in terms of distance modelling procedures, we suggest its use in practice.
机译：距离预测功能可用于各种应用中，以估计点之间的行进距离。为了评估距离预测功能的准确性并确定其参数，采用了拟合优度标准。 AD（绝对偏差），SD（平方偏差）和NAD（归一化绝对偏差）是实践中最常用的三个标准。在文献中，已经对每个标准的性质做出了一些假设。在本文中，我们介绍了进行统计分析以从不同角度比较这三个标准。为此，我们将？kpθ范数用作距离预测函数，并使用17个地理区域中的归一化绝对预测误差分布对三个准则进行统计比较。我们发现标准之间不存在显着差异。但是，由于标准SD在距离建模程序方面具有理想的属性，因此我们建议在实践中使用它。

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