BibTeX
@MISC{Marjerison06appliedstatistics,
author = {William M. Marjerison},
title = {Applied Statistics},
year = {2006}
}
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Abstract
We analyze data (length, weight and location) from a study done by the Army Corps of Engineers along the Tennessee River basin in the summer of 1980. The purpose is to predict the probability that a hypothetical channel catfish at a location studied is toxic and contains 5 ppm or more DDT in its filet. We incorporate spatial information and treate it separetely from other covariates. Ultimately, we want to predict the probability that a catfish from the unobserved location is toxic. In a preliminary analysis, we examine the data for observed locations using frequentist logistic regression, Bayesian logistic regression, and Bayesian logistic re-gression with random effects. Later we develop a parsimonious extension of Bayesian logistic regression and the corresponding Gibbs sampler for that model to increase computational feasibility and reduce model parameters. Furthermore, we develop a Bayesian model to impute data for locations where catfish were not observed. A comparison is made between results obtained fitting the model to only observed data and data with missing values imputed. Lastly, a complete model is presented which
Keyphrases
applied statistic bayesian logistic regression complete model bayesian model parsimonious extension observed location unobserved location random effect hypothetical channel catfish bayesian logistic re-gression army corp observed data frequentist logistic regression corresponding gibbs sampler computational feasibility tennessee river basin spatial information model parameter preliminary analysis
