The relationship between exposure to environmental chemicals during pregnancy and early childhood development is an important issue that has a spatial risk component. In this context, we have examined mental retardation and developmental delay (MRDD) outcome measures for children in a Medicaid population in South Carolina and sampled measures of soil chemistry (e.g. As, Hg, etc.) on a network o...

In the present paper, a Kriging-based metamodeling technique is used to minimize the risk of failure in a sheet metal forming process. The Krigingbased models are fitted to data that are obtained for larger experimental areas than the areas used in loworder polynomial regression metamodels. Therefore, computational time and memory requirement can be an obstacle for Kriging for data sets with ma...

As the development of earth sciences and cross-disciplinary, it’s more and more meaningful and valuable to analyze and estimate precipitation spatial distribution. Precipitation spatial information is important in many fields, such as water resource management, drought and flood disaster predication, and regional sustainable development. It’s unrealistic to get the accurate predication of a par...

Prediction of soil organic carbon (SOC) at unsampled locations is central to statistical modeling of regional SOC stocks. This is often accomplished by applying geostatistical techniques to plot inventory data. However, in many cases inventory data is sparsely sampled (<0.1 plots/km) relative to the region of interest, and it is unknown if geostatistics provides any advantage. Our objective was...

Estimation of ore reserves is one of the most critical aspects of mining geology. The accurate assessment of the tonnage and grade of run of mine may be the difference between a healthy profitable operation and an expensive early mine closure. The first step in ore reserve estimation is to determine the boundary of ore body or ore/waste contacts. This paper presents a specific mining applicatio...

When analyzing data from computationally expensive simulation codes, surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Kriging is a popular surrogate modeling technique used for the Design and Analysis of Computer Experiments (DACE). Hence, the past decade Kriging has been the subject of extensiv...

Kriging is a useful method for developing metamodels for product design optimization. The most popular kriging method, known as ordinary kriging, uses a constant mean in the model. In this article, a modified kriging method is proposed, which has an unknown mean model. Therefore it is called blind kriging. The unknown mean model is identified from experimental data using a Bayesian variable sel...

In multiple indicator models, there are several indicators for one trait–method unit. In the less restrictive model, there is one factor for all indicators belonging to the same trait–method unit. The correlations between these factors constitute a latent MTMM matrix. The correlation coefficients of this latent MTMM matrix are not distorted by measurement error and allow a more appropriate appl...

1. Abstract Over three decades, metamodeling has been widely applied to design optimization problems to build a surrogate model of computation-intensive engineering models. The Kriging method has gained significant interests for developing the surrogate model. However, traditional Kriging methods, including the ordinary Kriging and the universal Kriging, use fixed polynomials basis functions to...