This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 636
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #306994
Title: Gaussian Process Prediction of Computer Model Outputs with Nonconstant Variance in Systems Biology: A Plug-In Approach
Author(s): Garrett Dancik*+
Companies: Northwestern State University
Address: , , LA, 71497,
Keywords: Gaussian processes ; computer experiment

Gaussian processes (GPs) are statistical models commonly used to predict output from complex computer codes. Importantly, current GPs assume constant response variance, which is not true of all stochastic computer models. We propose a GP fitting scheme that uses 'plug-in' estimates of input-dependent variance components and where the GP is fit to a collection of sample mean outputs when replicate observations are made. For responses with both constant and non-constant variance, the proposed method is computationally more efficient and often more accurate than the standard approach which assumes constant variance and includes replicate observations directly in the GP fitting process. Results for a toy function and for a computer model of Leishmania infection are discussed. We implement our new model in the R package 'mlegp' which is available on the Comprehensive R Archive Network (CRAN).

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