Kriging (or Gaussian Process) metamodels may be analyzed through bootstrapping, which is a versatile statistical method but must be adapted to the speci.c problem being analyzed. More precisely, a random or discrete-event simulation may be run several times for the same scenario (combination of...

Distribution-free bootstrapping of the replicated responses of a given discreteevent simulation model gives bootstrapped Kriging (Gaussian process) metamodels; we require these metamodels to be either convex or monotonic. To illustrate monotonic Kriging, we use an M/M/1 queueing simulation with...

Kriging provides metamodels for deterministic and random simulation models. Actually, there are several types of Kriging; the classic type is so-called universal Kriging, which includes ordinary Kriging. These classic types require estimation of the trend in the input-output data of the...

The classic Kriging variance formula is widely used in geostatistics and in the design and analysis of computer experiments. This paper proves that this formula is wrong. Furthermore, it shows that the formula underestimates the Kriging variance in expectation. The paper develops parametric...