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首页> 外文会议>International joint conference on computational intelligence >Defuzzification of a Fuzzy p-value by the Signed Distance: Application on Real Data
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Defuzzification of a Fuzzy p-value by the Signed Distance: Application on Real Data

机译：通过符号距离对模糊p值进行模糊化：在实际数据上的应用

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We develop a fuzzy hypothesis testing approach where we consider the fuzziness of data and the fuzziness of the hypotheses as well. We give the corresponding fuzzy p-value with its α-cuts. In addition, we use the so-called "signed distance" operator to defuzzify this p-value and we provide the convenient decision rule. Getting a defuzzified p-value and being able to interpret it can be of good use in many situations. We illustrate our testing procedure by a detailed numerical example where we study a right one-sided fuzzy test and compare it with a classical one. We close the paper by an application of the method on a survey from the financial place of Zurich, Switzerland. We display the decisions related to tests on the mean made on a set of variables of the sample. Both fuzzy and classical tests are conducted. One of our main findings is that despite the fact that each of both approaches have a different decision rule in terms of interpretation, the decisions made are by far the same. In this perspective, we can state that the fuzzy testing procedure can be seen as a generalization of the classical one.
机译：我们开发了一种模糊假设检验方法，其中考虑了数据的模糊性以及假设的模糊性。我们给它对应的模糊p值及其α割。此外，我们使用所谓的“有符号距离”运算符对p值进行模糊化处理，并提供了方便的决策规则。在许多情况下，获取去模糊的p值并能够对其进行解释可能会很有用。我们通过一个详细的数值示例来说明我们的测试过程，在该示例中，我们研究了正确的单侧模糊测试，并将其与经典测试进行了比较。我们通过在瑞士苏黎世金融地进行的调查中应用该方法来结束本文。我们根据样本变量集上的平均值显示与检验相关的决策。进行了模糊测试和经典测试。我们的主要发现之一是，尽管事实上这两种方法在解释方面都有不同的决策规则，但到目前为止所做的决策是相同的。从这个角度来看，我们可以说模糊测试过程可以看作是对经典测试过程的概括。

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