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Prior-Free Probabilistic Prediction of Future Observations

Summary: [This article is based on the authors' abstract.] Prediction of future observations is a fundamental problem in statistics. Here we present a general approach based on the recently developed inferential model (IM) framework. We employ an IM-based technique to marginalize out the unknown parameters, yielding prior-free probabilistic prediction of future observables. Verifiable sufficient conditions are given for validity of our IM for prediction, and a variety of examples demonstrate the proposed method's performance. Thanks to its generality and ease of implementation, we expect that our IM-based method for prediction will be a useful tool for practitioners. Supplementary materials for this article are available online

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  • Topics: Statistics
  • Keywords: Prediction, Unknown parameters, Prior knowledge, Probability, Inference procedures
  • Author: Martin, Ryan; Lingham, Rama T.
  • Journal: Technometrics