Optimal designs for Gaussian process models |via spectral decomposition

Ofir Harari*, David M. Steinberg

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Gaussian processes provide a popular statistical modelling approach in various fields, including spatial statistics and computer experiments. Strategic experimental design could prove to be crucial when data are hard to collect. We use the Karhunen-Loève decomposition to study several popular design criteria. The resulting expressions are useful for understanding and comparing the criteria. A truncated form of the expansion is used to generate optimal designs. We give detailed results, including an error analysis, for the well-established integrated mean squared prediction error design criterion.

Original languageEnglish
Pages (from-to)87-101
Number of pages15
JournalJournal of Statistical Planning and Inference
Volume154
Issue number1
DOIs
StatePublished - 2014

Funding

FundersFunder number
US-Israel Binational Science Foundation2008218

    Keywords

    • Gaussian process
    • Optimal designs
    • Spectral decomposition

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