Timothy Hanson

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Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression(More)
We develop a novel semiparametric modeling framework involving mixtures of Polya trees for screening data with the dual purpose of diagnosing infection or disease status and of assessing the accuracy of continuous diagnostic measures. In this framework, we obtain (i) predictive probabilities of 'disease' based on continuous diagnostic test outcomes in(More)
We discuss the issue of identifiability of models for multiple dichotomous diagnostic tests in the absence of a gold standard (GS) test. Data arise as multinomial or product-multinomial counts depending upon the number of populations sampled. Models are generally posited in terms of population prevalences, test sensitivities and specificities, and test(More)
The evaluation of the performance of a continuous diagnostic measure is a commonly encountered task in medical research.We develop Bayesian non-parametric models that use Dirichlet process mixtures and mixtures of Polya trees for the analysis of continuous serologic data.The modelling approach differs from traditional approaches to the analysis of receiver(More)
The effect of spontaneous abortion on the dairy industry is substantial, costing the industry on the order of US dollars 200 million per year in California alone. We analyse data from a cohort study of nine dairy herds in Central California. A key feature of the analysis is the observation that only a relatively small proportion of cows will abort (around(More)
With increasing accessibility to Geographical Information Systems (GIS) software, researchers and administrators in public health are increasingly encountering spatially referenced datasets. Inferential interest of spatial data analysis often resides not in the statistically estimated maps themselves, but on the formal identification of “edges” or(More)