Full Bayesian significance test for extremal distributions

Diego F. de Bernardini; Laura L.R. Rifo

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Full Bayesian significance test for extremal distributions

机译：极值分布的完整贝叶斯显着性检验

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A new Bayesian measure of evidence is used for model choice within the generalized extreme value family of distributions, given an absolutely continuous posterior distribution on the related parametric space. This criterion allows quantitative measurement of evidence of any sharp hypothesis, with no need of a prior distribution assignment to it. We apply this methodology to the testing of the precise hypothesis given by the Gumbel model using real data. Performance is compared with usual evidence measures, such as Bayes factor, Bayesian information criterion, deviance information criterion and descriptive level for deviance statistic.

机译：给定相关参数空间上的绝对连续后验分布，在广义极值分布族内使用新的贝叶斯证据度量进行模型选择。该标准允许对任何尖锐假设的证据进行定量测量，而无需对其进行事先分配。我们将此方法应用于使用实际数据对Gumbel模型给出的精确假设进行检验。将性能与常规证据度量（例如贝叶斯因子，贝叶斯信息准则，偏差信息准则和偏差统计的描述水平）进行比较。

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来源
《Journal of applied statistics》 |2011年第4期|p.851-863|共13页
作者
Diego F. de Bernardini; Laura L.R. Rifo;
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作者单位

Institute of Mathematics, University of Campinas - UNICAMP, Brazil;

Institute of Mathematics, University of Campinas - UNICAMP, Brazil;

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正文语种 eng
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关键词
bayesian inference; credible intervals; extremal-type distribution; full bayesian significance test; predictive return level;

机译：贝叶斯推理可靠的间隔;极值型分布完整的贝叶斯显着性检验;预期收益水平;

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Full Bayesian significance test for extremal distributions

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