This paper investigates the use of Kriging in random simulation when the simulation output variances are not constant. Kriging gives a response surface or metamodel that can be used for interpolation. Because Ordinary Kriging assumes constant variances, this paper also applies Detrended Kriging to estimate a non-constant signal function, and then standardizes the residual noise through the heterogeneous variances estimated from replicated simulation runs. Numerical examples, however, suggest that Ordinary Kriging is a robust interpolation method
Many discrete simulation optimization techniques are unsuitable when the number of feasible solution...
The issue of smoothing in kriging has been addressed either by estimation or simulation. The solutio...
This paper proposes a novel method to select an experimental design for interpolation in random simu...
This paper investigates the use of Kriging in random simulation when the simulation output variances...
This paper investigates the use of Kriging in random simulation when the simulation output variances...
Many scientific disciplines use mathematical models to describe complicated real systems. Often, ana...
Kriging provides metamodels for deterministic and random simulation models. Actually, there are seve...
Kriging provides metamodels for deterministic and random simulation models. Actually, there are seve...
We extend the basic theory of kriging, as applied to the design and analysis of deterministic comput...
We study the estimation of the true variance of the predictor in stochastic Kriging (SK). First, we ...
We study the correct estimation of the true variance of the predictor in stochastic Kriging (SK). Fi...
Scientists and engineers commonly use simulation models to study real systems for which actual exper...
In the field of the Design and Analysis of Computer Experiments (DACE) meta-models are used to appro...
This paper proposes a novel method to select an experimental design for interpolation in random simu...
This paper investigates two related questions: (1) How to derive a confidence interval for the outpu...
Many discrete simulation optimization techniques are unsuitable when the number of feasible solution...
The issue of smoothing in kriging has been addressed either by estimation or simulation. The solutio...
This paper proposes a novel method to select an experimental design for interpolation in random simu...
This paper investigates the use of Kriging in random simulation when the simulation output variances...
This paper investigates the use of Kriging in random simulation when the simulation output variances...
Many scientific disciplines use mathematical models to describe complicated real systems. Often, ana...
Kriging provides metamodels for deterministic and random simulation models. Actually, there are seve...
Kriging provides metamodels for deterministic and random simulation models. Actually, there are seve...
We extend the basic theory of kriging, as applied to the design and analysis of deterministic comput...
We study the estimation of the true variance of the predictor in stochastic Kriging (SK). First, we ...
We study the correct estimation of the true variance of the predictor in stochastic Kriging (SK). Fi...
Scientists and engineers commonly use simulation models to study real systems for which actual exper...
In the field of the Design and Analysis of Computer Experiments (DACE) meta-models are used to appro...
This paper proposes a novel method to select an experimental design for interpolation in random simu...
This paper investigates two related questions: (1) How to derive a confidence interval for the outpu...
Many discrete simulation optimization techniques are unsuitable when the number of feasible solution...
The issue of smoothing in kriging has been addressed either by estimation or simulation. The solutio...
This paper proposes a novel method to select an experimental design for interpolation in random simu...