Skip to main content
Article thumbnail
Location of Repository

DOES MORE UNIFORMLY DISTRIBUTED SAMPLING GENERALLY LEAD TO MORE ACCURATE PREDICTION IN COMPUTER EXPERIMENTS?

By M. E. Kuhl, N. M. Steiger, F. B. Armstrong, J. A. Joines, Longjun Liu and Wayne Wakeland

Abstract

Sampling uniformity is one of the central issues for computer experiments or metamodeling. Is it generally true that more uniformly distributed sampling leads to more accurate prediction? A study was conducted to compare four designs for computer experiments, based on simulation tests and statistical analysis. Maximin Latin hypercube design (LHMm) nearly always generated more uniform sampling in two- and three- dimensional cases than does random sampling (Rd), Latin hypercube design (LHD), or Minimized centered L2 discrepancy Latin hypercube design (LHCL2). But often there was no significant difference among the means of the prediction errors by employing LHMm versus the other designs. Occasionally, even the opposite was seen. More uniform sampling did not generally lead to more accurate prediction unless sampling included extremely nonuniform cases, especially when the sample size was relatively small.

Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.415.5162
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.informs-sim.org/wsc... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.