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Adjusted Bayesian inference for selected parameters
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DOI：10.1111/j.1467-9868.2011.01016.x.png)
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摘要
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. We address the problem of providing inference from a Bayesian perspective for parameters selected after viewing the data. We present a Bayesian framework for providing inference for selected parameters, based on the observation that providing Bayesian inference for selected parameters is a truncated data problem. We show that if the prior for the parameter is non-informative, or if the parameter is a fixed unknown constant, then it is necessary to adjust the Bayesian inference for selection. Our second contribution is the introduction of Bayesian false discovery rate controlling methodology, which generalizes existing Bayesian false discovery rate methods that are only defined in the two-group mixture model. We illustrate our results by applying them to simulated data and data from a microarray experiment.
Keyword:
Bayesian false discovery rate
Directional decisions
False discovery rate
Selection bias
Selective inference
期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
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分享收藏The positive false discovery rate:: A Bayesian interpretation and the q-value.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
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