The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimizat...

February 19, 2015 Version 3.0.62 Title Simulation and Analysis of Random Fields Author Martin Schlather [aut, cre], Alexander Malinowski [aut], Marco Oesting [aut], Daphne Boecker [aut], Kirstin Strokorb [aut], Sebastian Engelke [aut], Johannes Martini [aut], Felix Ballani [aut], Peter Menck [ctr], Sebastian Gross [ctr], Ulrike Ober [ctb], Katharina Burmeister [ctb], Juliane Manitz [ctb], Paulo...

Metamodelling offers an efficient way to imitate the behaviour of computationally expensive simulators. Kriging based metamodels are popular in approximating computation-intensive simulations of deterministic nature. Irrespective of the existence of various variants of Kriging in the literature, only a handful of Kriging implementations are publicly available and most, if not all, free librarie...

Metamodeling, i.e. building surrogate models to expensive blackbox functions, is an interesting way to reduce the computational burden for optimization purpose. Kriging is a popular metamodel based on Gaussian Process theory, whose statistical properties have been exploited to build efficient global optimization algorithms. Single and multi-objective extensions have been proposed to deal with c...

Geostatistical models often require a variogram or covariance model for kriging and krigingbased simulation. Next to the initial decision of stationarity, the choice of an appropriate variogram model is the most important decision in a geostatistical study. Common practice consists of fitting experimental variograms with a nested combination of proven models such as the spherical, exponential, ...

In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Krigin...

In this paper we introduce a new interpolation method, known as kernel interpolation (KI), for modeling the output from expensive deterministic computer experiments. We construct it by repeating a generalized version of the classic Nadaraya-Watson kernel regression an infinite number of times. Although this development is numerical, we are able to provide a statistical framework for KI using a ...

Microwave remote sensing such as soil moisture active passive (SMAP) can provide data for agricultural and hydrological studies. However, the scales between station-measured satellite-measured products are quite different, stations measure on a point scale while satellites have much larger footprint (e.g., 9 km). Consequently, validation products, especially inter-comparison these two types of ...

When analysing data from computationally expensive simulation codes or process measurements, surrogate modelling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualisation and optimisation. Kriging is a popular surrogate modelling technique for data based on deterministic computer experiments. There exist several types of Kriging, mostly dif...

This article reviews Kriging (also called spatial correlation modeling). It presents the basic Kriging assumptions and formulas—contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor. Besides classic one-shot statistical designs such as Latin Hypercube Sampl...