Abstract Details
Activity Number:
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698
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Type:
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Contributed
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Date/Time:
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Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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SSC
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Abstract - #308417 |
Title:
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Imprecise Truncated Poisson Regression for Predictive Inference
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Author(s):
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Chel Hee Lee*+ and Mikelis Bickis
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Companies:
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University of Saskatchewan and University of Saskatchewan
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Keywords:
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prior ignorance ;
imprecise probability ;
zero-truncated Poisson regression
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Abstract:
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Prevalence is a valuable epidemiological measure about the burden of disease in a community for planning health services; however, the true prevalence is typically underestimated. Since there is no way to know the truth in practice, we aim to construct a framework for quantifying our epistemic ignorance about the estimate by applying the Walley's inferential paradigm. We restricted ourselves to zero-truncated Poisson sampling models that give an one-parameter exponential family with the canonical log-link function since zero counts are not observed. Normal and log-gamma priors are mainly studied on the canonical hyperparameter space by constructing a three-parameter exponential family of distributions which includes both priors. A canonical parametrization allows us to incorporate information about covariates into the model by equating them to a linear combination of regression parameters; thus, normal priors on regression parameters induce normal priors on canonical parameter in the form of a multiple parameter exponential family. Finally, we visualize a translation of this family of posteriors on the hyperparameter space and a decrease of prior ignorance by learning from data.
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Authors who are presenting talks have a * after their name.
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