Penalized blind kriging in computer experiments

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Abstract

Kriging models are popular in analyzing computer experiments. The most widely used kriging models apply a constant mean to capture the overall trend. This method can lead to a poor prediction when strong trends exist. To tackle this problem, a new modeling method is proposed, which incorporates a variable selection mechanism into kriging via a penalty function. An efficient algorithm is introduced and oracle properties in terms of selecting the correct mean function are derived according to fixed-domain asymptotics. The finite-sample performance is examined via a simulation study. Application of the proposed methodology to circuit-simulation experiments demonstrates a remarkable improvement in prediction, and the capability of identifying variables that most affect the system.

Original languageEnglish (US)
Pages (from-to)1171-1190
Number of pages20
JournalStatistica Sinica
Volume21
Issue number3
DOIs
StatePublished - Jul 1 2011

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Computer model
  • Fixed-domain asymptotics
  • Gaussian process model
  • Geostatistics
  • Oracle procedure
  • Variable selection

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