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Full-Text Articles in Physical Sciences and Mathematics
Hierarchical Model Building, Fitting, And Checking: A Behind-The-Scenes Look At A Bayesian Analysis Of Arsenic Exposure Pathways, Peter F. Craigmile, Catherine A. Calder, Hongfei Li, Rajib Paul, Noel Cressie
Hierarchical Model Building, Fitting, And Checking: A Behind-The-Scenes Look At A Bayesian Analysis Of Arsenic Exposure Pathways, Peter F. Craigmile, Catherine A. Calder, Hongfei Li, Rajib Paul, Noel Cressie
Professor Noel Cressie
In this article, we present a behind-the-scenes look at a Bayesian hierarchical analysis of pathways of exposure to arsenic (a toxic heavy metal) using the Phase I National Human Exposure Assessment Survey carried out in Arizona. Our analysis combines individual-level personal exposure measurements (biomarker and environmental media) with water, soil, and air observations from the ambient environment. We include details of our model-building exercise that involved a combination of exploratory data analysis and substantive knowledge in exposure science. Then we present our strategies for model fitting, which involved piecing together components of the hierarchical model in a systematic fashion to …
Accounting For Uncertainty In Ecological Analysis: The Strengths And Limitations Of Hierarchical Statistical Modeling, Noel Cressie, Catherine Calder, James Clark, Jay Ver Hoef, Christopher Wikle
Accounting For Uncertainty In Ecological Analysis: The Strengths And Limitations Of Hierarchical Statistical Modeling, Noel Cressie, Catherine Calder, James Clark, Jay Ver Hoef, Christopher Wikle
Professor Noel Cressie
Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple …