Support vector regression builds a model of a process that depends on a set of factors. It traditionally considers one output at a time, which means that advantage cannot be taken of the correlations that may exist between outputs. The purpose of this paper is to show how the body of knowledge accumulated by geostatisticians on Kriging and its extensions over the last 40 years can help extend s...

Despite advances in computer capacity, the enormous computational cost of running complex engineering simulations makes it impractical to rely exclusively on simulation for the purpose of design optimization. To cut down the cost, surrogate models, also known as metamodels, are constructed from and then used in place of the actual simulation models. In the paper, we systematically compare four ...

A simple algorithm for estimating the regression function over the United States is introduced. The approach allows for data obtained from a complicated sampling design, as well as for the inclusion of a few additional covariates. The regression estimates are obtained from an associated probability density estimate, namely the averaged shifted histogram. The algorithm has proven especially succ...

BACKGROUND Because of the warming climate urban temperature patterns have been receiving increased attention. Temperature within urban areas can vary depending on land cover, meteorological and other factors. High resolution satellite data can be used to understand this intra-urban variability, although they have been primarily studied to characterize urban heat islands at a larger spatial scal...

International Journal of Geographical Information Science Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713599799 Population-density estimation using regression and area-to-point residual kriging X. H. Liu a; P. C. Kyriakidis b; M. F. Goodchild b a Department of Geography & Human Environmental Studies, San F...

Sequential parameter optimization is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms. It includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging), and combinations of different meta-modeling approaches. Th...

The precision agriculture hopes to manage the variation in soil nutrient status continuously, which requires reliable predictions at places between sampling sites. For the long time, ordinary kriging has been used as one prediction method when the data are spatially dependent and a suitable variogram model exists. However, even if data are spatially correlated, there are often few soil sampling...