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Multi-Resolution Spatial Random-Effects Models for Irregularly Spaced Data by Tzeng, ShengLi; Huang, Hsin-Cheng is a scholarly article available to read on EtoBox.

What is Multi-Resolution Spatial Random-Effects Models for Irregularly Spaced Data about?

The spatial random-effects model is flexible in modeling spatial covariance functions, and is computationally efficient for spatial prediction via fixed rank kriging. However, the success of this model depends on an appropriate set of basis functions. In this research, we propose a class of basis functions extracted from thin-plate splines. These functions are ordered in terms of their degrees of smoothness with a higher-order function corresponding to larger-scale features and a lower-order one corresponding to smaller-scale details, leading to a parsimonious representation for a nonstationary spatial covariance function. Consequently, only a small to moderate number of functions are needed in a spatial random-effects model. The proposed class of basis functions has several advantages over commonly used ones. First, we do not need to concern about the allocation of the basis functions, but simply select the total number of functions corresponding to a resolution. Second, only a small number of basis functions is usually required, which facilitates computation. Third, estimation variability of model parameters can be considerably reduced, and hence more precise covariance function

Author

Tzeng, ShengLi; Huang, Hsin-Cheng

Published

2015

Language

EN