Data analytic tools for understanding random field regression models

David M. Steinberg*, Dizza Bursztyn

*Corresponding author for this work

Research output: Contribution to specialist publicationArticle

20 Scopus citations


Random field regression (RFR) models, in which a response is treated as the realization of a random field, have been advocated for modeling data from experiments in high signal-to-noise settings. In particular, RFR models have proven useful in analyzing data generated from computer simulations of complex processes. They offer flexibility for smoothing these data and are able to interpolate the known values for factor settings tested on the simulator. However, these models lack the easy interpretability of standard regression estimators. Our purpose in this article is to demonstrate that there is actually much common ground between the RFR models and Bayesian regression and to provide some simple data-analytic tools that can help expose a regression model associated with an RFR model.

Original languageAmerican English
Number of pages10
Specialist publicationTechnometrics
StatePublished - Nov 2004
Externally publishedYes

Bibliographical note

Funding Information:
This research was supported by a grant from the Israel Science Foundation. The authors thank the referees, the editor, and the associate editor for many helpful comments that improved the article.


  • Bayesian regression
  • Computer experiments
  • Gaussian covariance
  • Polynomial regression
  • Spline regression


Dive into the research topics of 'Data analytic tools for understanding random field regression models'. Together they form a unique fingerprint.

Cite this