具有Hölder约束的多正态均值推断的部分条件化
Partial Conditioning for Inference of Many-Normal-Means with Hölder Constraints
AI总结：
针对具有Hölder约束的多正态均值问题，提出部分条件化方法以生成有效且高效的边际推断，在有效性和效率上均优于现有方法。
摘要：
推断模型已被提出用于有效且高效的无先验概率推断。随着该理论逐渐普及，它需要针对实际具有挑战性的问题进行进一步发展。本文考虑了均值被约束在彼此邻域内的多正态均值问题，该约束由Hölder空间形式化表示。提出了一种称为部分条件化的新方法，用于生成关于个体均值的有效且高效的边际推断。结果表明，该方法在有效性上优于基准方法，在效率上优于保守方法。我们通过指出部分条件化推断模型的一般理论值得未来发展来结束本文。
英文摘要：
Inferential models have been proposed for valid and efficient prior-free probabilistic inference. As it gradually gained popularity, this theory is subject to further developments for practically challenging problems. This paper considers the many-normal-means problem with the means constrained to be in the neighborhood of each other, formally represented by a Hölder space. A new method, called partial conditioning, is proposed to generate valid and efficient marginal inference about the individual means. It is shown that the method outperforms both a fiducial-counterpart in terms of validity and a conservative-counterpart in terms of efficiency. We conclude the paper by remarking that a general theory of partial conditioning for inferential models deserves future development.
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