JSM 2005 - Toronto

Abstract #303661

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 142
Type: Contributed
Date/Time: Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #303661
Title: Statistical Models for Computer Experiment Output Having Qualitative Input Variables
Author(s): Gang Han*+ and Thomas J. Santner and William I. Notz
Companies: The Ohio State University and The Ohio State University and The Ohio State University
Address: 618 Harley Dr Apt1, Columbus, OH, 43202, United States
Keywords: calibration ; Gaussian Stochastic Process ; Perk ; power exponential correlation
Abstract:

The problem of fitting statistical models to approximate the output of a complex computer code has been of considerable interest to researchers. Predictors based on Gaussian stochastic process models have been of particular interest for the case of continuous input variables. We propose statistical models for prediction that allow the computer model inputs to be both continuous and qualitative. We also implement and compare a frequentist approach and a Bayesian formulation in several examples.

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