JSM 2005 - Toronto

Abstract #302420

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 307
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 2:00 PM to 3:50 PM
Sponsor: Technometrics
Abstract - #302420
Title: Some Simple Data Analytic Tools for Understanding Random Field Regression Models
Author(s): David M. Steinberg*+ and Dizza Bursztyn
Companies: Tel Aviv University and Ashkelon College
Address: Department of Statistics and Operations Research, Tel Aviv, 69978, Israel
Keywords: Computer Experiments ; Gaussian Processes ; Nuclear Waste Disposal ; Principal Components ; Spatial Smoothing
Abstract:

Many processes are studied today using computer simulators rather than laboratory experiments or field studies. For example, we illustrate the ideas in this talk using results from a sensitivity analysis of a nuclear waste repository. The longtime scales of interest can be studied only by simulators. Challenging problems arise in modeling data from computer experiments. One popular class of models is random field regression models (also known as Gaussian stochastic process models). The idea behind these models is to treat the output from an experiment as the realization of a Gaussian process. The covariance function of the process depends on the input factors and unknown parameters estimated from the data. Responses at further input settings can be estimated by their BLUP's. These models have proven successful in applications, but can be difficult to interpret. In this talk, we will show that random field regression models have a natural interpretation as Bayesian regression models. We will present data analytic tools that make it possible to discover the associated regression model. Finally, we will apply the ideas to the nuclear waste study.

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Revised March 2005