| Conditionally Optimal Weights of Evidence |
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| 作者姓名： | StephanMorgenthaler RobertG.Staudte |
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| 作者单位： | Ecole polytechnique federale de Lausanne (EPFL)，SB IMA，Station 8，1015 Lausanne，Switzerland，Statistics Department，LaTrobe University，Melbourne，3086，Australia |
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| 基金项目： | Supported in part by a grant from the Swiss National Science Foundation. |
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| 摘 要： | A weight of evidence is a calibrated statistic whose values in 0,1] indicate the degree of agreement between the data and either of two hypothesis, one being treated as the null (H_0) and the other as the alternative (H_1). A value of zero means perfect agreement with the null, whereas a value of one means perfect agreement with the alternative. The optimality we consider is minimal mean squared error (MSE) under the alternative while keeping the MSE under the null below a fixed bound. This paper studies such statistics from a conditional point of view, in particular for location and scale models.
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| 关 键 词： | 结构模型 不变量 置信区间分布 随机变量 统计测试 |
| 收稿时间： | 20 March 2004 |
Conditionally Optimal Weights of Evidence |
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| StephanMorgenthaler RobertG.Staudte.Conditionally Optimal Weights of Evidence[J].Acta Mathematicae Applicatae Sinica,2005,21(2):247-256. |
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| Authors: | Stephan Morgenthaler Robert G Staudte |
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| Abstract: | A weight of evidence is a calibrated statistic whose values in 0,1] indicate the degree of agreement between the data and either of two hypothesis, one being treated as the null (H_0) and the other as the alternative (H_1). A value of zero means perfect agreement with the null, whereas a value of one means perfect agreement with the alternative. The optimality we consider is minimal mean squared error (MSE) under the alternative while keeping the MSE under the null below a fixed bound. This paper studies such statistics from a conditional point of view, in particular for location and scale models. |
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| Keywords: | Structural models invariance confidence distributions testing |
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