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Adaptive multigroup confidence intervals with constant coverage

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  • C Yu
  • P D Hoff

Abstract

SUMMARYCommonly used interval procedures for multigroup data attain their nominal coverage rates across a population of groups on average, but their actual coverage rate for a given group will be above or below the nominal rate, depending on the group mean. While correct coverage for a given group can be achieved with a standard $t$-interval, this approach is not adaptive to the available information about the distribution of group-specific means. In this article we construct confidence intervals that have a constant frequentist coverage rate and that make use of information about across-group heterogeneity, resulting in constant-coverage intervals that are narrower than standard $t$-intervals on average across groups. Such intervals are constructed by inverting biased Bayes-optimal tests for the mean of each group, where the prior distribution for a given group is estimated with data from the other groups.

Suggested Citation

  • C Yu & P D Hoff, 2018. "Adaptive multigroup confidence intervals with constant coverage," Biometrika, Biometrika Trust, vol. 105(2), pages 319-335.
  • Handle: RePEc:oup:biomet:v:105:y:2018:i:2:p:319-335.
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    File URL: http://hdl.handle.net/10.1093/biomet/asy009
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    Cited by:

    1. Aaron Osgood‐Zimmerman & Jon Wakefield, 2023. "A Statistical Review of Template Model Builder: A Flexible Tool for Spatial Modelling," International Statistical Review, International Statistical Institute, vol. 91(2), pages 318-342, August.

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