The kriging update model and recursive space-time function estimation

The kriging update model and recursive space-time function estimation

We present a method for efficiently fitting a time series of spatial functions to observed data. The method is closely related to kriging, which is an interpolation method based on a stochastic data model. While kriging is effective and versatile for estimating individual functions from observed data, it must be extended to incorporate temporal correlation. In this paper, we introduce temporal ...

Recursive Multiscale Estimation of Space-Time Random Fields

We recently developed a multiscale recursive estimation procedure for the estimation of large-scale dynamic systems. The procedure propagates multiscale models for the estimation errors more e ciently than the Kalman lter's propagation of the error covariances, with a resulting computational complexity of O(N ) and O N , where N is the number of variables estimated, for 1D and 2-D dynamic syste...

Kriging with nonparametric variance function estimation.

A method for fitting regression models to data that exhibit spatial correlation and heteroskedasticity is proposed. It is well known that ignoring a nonconstant variance does not bias least-squares estimates of regression parameters; thus, data analysts are easily lead to the false belief that moderate heteroskedasticity can generally be ignored. Unfortunately, ignoring nonconstant variance whe...

Sampling Design Optimization for Space-Time Kriging

Space-time kriging extension of precipitation variability