Interval Estimation for Messy Observational Data

Details Interval Estimation for Messy Observational Data Interval Estimation for Messy Observational Data

2010-10-02

arxiv.org/abs/1010.0306v1

Statistical Science 2009, Vol. 24, No. 3, 328-342

Statistics

Methodology

Published in at http://dx.doi.org/10.1214/09-STS305 the Statistical Science (http://www.imstat.org/sts/) by the Institute of M

Scientific paper

10.1214/09-STS305

We review some aspects of Bayesian and frequentist interval estimation, focusing first on their relative strengths and weaknesses when used in "clean" or "textbook" contexts. We then turn attention to observational-data situations which are "messy," where modeling that acknowledges the limitations of study design and data collection leads to nonidentifiability. We argue, via a series of examples, that Bayesian interval estimation is an attractive way to proceed in this context even for frequentists, because it can be supplied with a diagnostic in the form of a calibration-sensitivity simulation analysis. We illustrate the basis for this approach in a series of theoretical considerations, simulations and an application to a study of silica exposure and lung cancer.

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