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The Missing Data Problem for a Two-Dimensional Surface

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Advanced Spatial Statistics

Part of the book series: Advanced Studies in Theoretical and Applied Econometrics ((ASTA,volume 12))

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Abstract

The topics, themes and subject matter of Chapters 1 thru 5 are of a reference nature. Besides the complexities discussed in these preceding chapters, the field of spatial statistics also embraces several prominent difficult, bothersome, and unresolved problems. One concern has to do with incomplete data, which is the topic of this chapter. Geographical data sets sometimes contain missing observations that need to be estimated. An exact maximum likelihood solution for this problem is discussed, both in terms of parameter and missing value estimation, for multivariate normal spatial data sets satisfying the first-order spatial Markov property with constant mean. Moreover, information at neighboring or contiguous observed sites is used to estimate the missing values, and then the complete spatial distribution is used to estimate model parameters. The solution procedure is iterative, and is akin to the Orchard and Woodbury missing information principle. Results are reported for extensions to a second-order, simultaneous model, and from an empirical example used to explore the behavior of these estimates. Also, tentative simulation experiment findings are reviewed.

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  1. University of New York, Buffalo, USA

    Daniel A. Griffith

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© 1988 Kluwer Academic Publishers, Dordrecht

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Griffith, D.A. (1988). The Missing Data Problem for a Two-Dimensional Surface. In: Advanced Spatial Statistics. Advanced Studies in Theoretical and Applied Econometrics, vol 12. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-2758-2_6

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  • DOI: https://doi.org/10.1007/978-94-009-2758-2_6

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