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Activity Number:
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244
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #308317 |
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Title:
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A Bayesian Approach to a Logistic Regression Model with Incomplete Information
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Author(s):
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Taeryon Choi*+ and Mark J. Schervish and Ketra Schmitt and Mitchell Small
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Companies:
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University of Maryland, Baltimore County and Carnegie Mellon University and Battelle Memorial Institute and Carnegie Mellon University
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Address:
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1000 Hilltop circle, Baltimore, MD, 21250,
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Keywords:
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Aggregate information ; Dose-response study ; Logistic regression ; MCMC ; WinBUGS
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Abstract:
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We consider a set of independent Bernoulli trials with possibly different success probabilities that depend on covariate values. However, the available data consist only of aggregate numbers of successes amongst subsets of the trials along with all of the covariate values. We still wish to estimate the parameters of a modeled relationship between the covariates and the success probabilities (e.g., a linear logistic regression model). In this paper, estimation of the parameters is made from a Bayesian perspective by using a Markov Chain Monte Carlo (MCMC) algorithm based only on the available data. The proposed methodology is applied to both simulation studies and real data from a dose-response study of a toxic chemical, perchlorate.
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