Norms of inverses and condition numbers for matrices associated with scattered data
Journal of Approximation Theory
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Eigenvalues of Euclidean distance matrices
Journal of Approximation Theory
Norm estimates for inverses of Euclidean distance matrices
Journal of Approximation Theory
Lower bounds for norms of inverses of interpolation matrices for radial basis functions
Journal of Approximation Theory
Genetic algorithms and tabu search: hybrids for optimization
Computers and Operations Research - Special issue on genetic algorithms
Algorithmic geometry
An algorithm for the construction of spatial coverage designs with implementation in SPLUS
Computers & Geosciences
Latin hypercube sampling of Gaussian random fields
Technometrics
Optimal exact experimental designs with correlated errors through a simulated annealing algorithm
Computational Statistics & Data Analysis
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Maximin Latin Hypercube Designs in Two Dimensions
Operations Research
Survey paper: Optimal experimental design and some related control problems
Automatica (Journal of IFAC)
Two-dimensional minimax Latin hypercube designs
Discrete Applied Mathematics
Comparing designs for computer simulation experiments
Proceedings of the 40th Conference on Winter Simulation
Design and Analysis of Simulation Experiments
Design and Analysis of Simulation Experiments
Bounds for Maximin Latin Hypercube Designs
Operations Research
Optimal designs for parameter estimation of the Ornstein–Uhlenbeck process
Applied Stochastic Models in Business and Industry
Optimal design for correlated processes with input-dependent noise
Computational Statistics & Data Analysis
Journal of Multivariate Analysis
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When setting up a computer experiment, it has become a standard practice to select the inputs spread out uniformly across the available space. These so-called space-filling designs are now ubiquitous in corresponding publications and conferences. The statistical folklore is that such designs have superior properties when it comes to prediction and estimation of emulator functions. In this paper we want to review the circumstances under which this superiority holds, provide some new arguments and clarify the motives to go beyond space-filling. An overview over the state of the art of space-filling is introducing and complementing these results.