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Combining Information in Statistical Modeling

Daniel Pena
The American Statistician
Vol. 51, No. 4 (Nov., 1997), pp. 326-332
DOI: 10.2307/2685900
Stable URL: http://www.jstor.org/stable/2685900
Page Count: 7
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Combining Information in Statistical Modeling
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Abstract

How to combine information from different sources is becoming an important statistical area of research under the name of Meta-Analysis. This paper shows that the estimation of a parameter or the forecast of a random variable can also be seen as a process of combining information. It is shown that this approach can provide some useful insights on the robustness properties of some statistical procedures, and it also allows the comparison of statistical models within a common framework. Some general combining rules are illustrated using examples from ANOVA analysis, diagnostics in regression, time series forecasting, missing value estimation, and recursive estimation using the Kalman filter.

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