• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistical Methods and Applications >Blending Bayesian and frequentist methods according to the precision of prior information with applications to hypothesis testing
【24h】

Blending Bayesian and frequentist methods according to the precision of prior information with applications to hypothesis testing

机译：根据先验信息的精确度将贝叶斯方法和频率论方法融合在一起，并应用于假设检验

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The proposed minimax procedure blends strict Bayesian methods with p values and confidence intervals or with default-prior methods. Two applications to hypothesis testing bring some implications to light. First, the blended probability that a point null hypothesis is true is equal to the p value or a lower bound of an unknown posterior probability, whichever is greater. As a result, the p value is reported instead of any posterior probability in the case of complete prior ignorance but is ignored in the case of a fully known prior. In the case of partial knowledge about the prior, the possible posterior probability that is closest to the p value is used for inference. The second application provides guidance on the choice of methods used for small numbers of tests as opposed to those appropriate for large numbers. Whereas statisticians tend to prefer a multiple comparison procedure that adjusts each p value for small numbers of tests, large numbers instead lead many to estimate the local false discovery rate (LFDR), a posterior probability of hypothesis truth. Each blended probability reduces to the LFDR estimate if it can be estimated with sufficient accuracy or to the adjusted p value otherwise.
机译：提出的minimax过程将严格的贝叶斯方法与p值和置信区间或默认优先方法混合在一起。假设检验的两种应用带来了一些启示。首先，点零假设为真的混合概率等于p值或未知后验概率的下限，以较大者为准。结果，在完全先验无知的情况下报告了p值，而不是任何后验概率，但是在众所周知的先验情况下被忽略。在对先验有部分了解的情况下，将最接近p值的可能后验概率用于推理。第二个应用程序提供了用于少量测试的方法选择指南，而不是适合大量测试的方法。统计人员倾向于采用多重比较程序来针对少量测试调整每个p值，而大量测试则导致许多人估计局部错误发现率（LFDR），这是假设真相的后验概率。如果可以以足够的精度估计每个混合概率，则将其减少到LFDR估计，否则将减少到调整后的p值。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号