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Combining information from independent sources through confidence distributions. (English) Zbl 1064.62003

Summary: This paper develops new methodology, together with related theories, for combining information from independent studies through confidence distributions. A formal definition of a confidence distribution and its asymptotic counterpart (i.e., asymptotic confidence distribution) are given and illustrated in the context of combining information. Two general combination methods are developed: the first along the lines of combining \(p\)-values, with some notable differences in regard to optimality of Bahadur type efficiency; the second by multiplying and normalizing confidence densities. The latter approach is inspired by the common approach of multiplying likelihood functions for combining parametric information.
The paper also develops adaptive combining methods, with supporting asymptotic theory which should be of practical interest. The key point of the adaptive development is that the methods attempt to combine only the correct information, downweighting or excluding studies containing little or wrong information about the true parameter of interest. The combination methodologies are illustrated in simulated and real data examples with a variety of applications.

MSC:

62A01 Foundations and philosophical topics in statistics
65C60 Computational problems in statistics (MSC2010)
62F03 Parametric hypothesis testing
62F12 Asymptotic properties of parametric estimators
62G05 Nonparametric estimation
62G10 Nonparametric hypothesis testing
62G20 Asymptotic properties of nonparametric inference
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References:

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