Combining information from independent sources through confidence distributions

By Kesar Singh, Min‐ge Xie, William E. Strawderman. 2005

165 citations recorded by the source corpus.

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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8ddbe53901a668c7387ddb1b4e963db137b77a4a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Jan Hannig
2. Randy C. S. Lai
3. Hari Iyer
4. Thomas C.M. Lee
5. Min‐ge Xie
6. Xueying Chen
7. Ryan Martin
8. Lan Wang

Citations in the corpus

• Generalized Fiducial Inference: A Review and New Results (2016, background): “…approach is fusion learning: its ability to combine information from disparate sources with deep implications for meta analysis (Schweder and Hjort 2002; Singh, Xie, and Strawderman 2005; Xie, Singh, and Strawderman 2011; Hannig and © American Statistical Association Xie 2012; Xie et al. 2013).”
• A Split-and-Conquer Approach for Analysis of Extraordinarily Large Data (2014, background): “According to Singh, Xie, and Strawderman (2005), Xie, Singh, Strawderman (2011), and Liu (2012), equivalent combined statistics or asymptotic efficiency are achievable for many other models.”