An Innovations Approach for Interpolating a Random Surface Based on Sparse Data

A. V. Vecchia
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A. V. Vecchia: Denver Federal Center, United States Geological Survey Water Resources Division

A chapter in Computing Science and Statistics, 1992, pp 215-224 from Springer

Abstract: Abstract Innovations methods have become exceedingly useful in time series analysis for parameter estimation and prediction. However, it is difficult to generalize innovations techniques to spatial random processes, primarily due to the lack of a natural ordering of observations in the spatial domain. In this paper, an innovations approach is proposed for interpolating a spatial random process based on a sparse set of irregularly spaced observations. The process is assumed to be a stationary, two-dimensional Gaussian random field. The proposed interpolation method is demonstrated on both simulated and actual data.

Keywords: Royal Statistical Society; Observation Location; Innovation Approach; Standardize Innovation; Good Linear Predictor (search for similar items in EconPapers)
Date: 1992
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-2856-1_28

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DOI: 10.1007/978-1-4612-2856-1_28

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