Correcting for informative sampling in spatial covariance estimation and kriging predictions

Erin M. Schliep (), Christopher K. Wikle and Ranadeep Daw
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Erin M. Schliep: University of Missouri
Christopher K. Wikle: University of Missouri
Ranadeep Daw: University of Missouri

Journal of Geographical Systems, 2023, vol. 25, issue 4, No 7, 587-613

Abstract: Abstract Informative sampling designs can impact spatial prediction, or kriging, in two important ways. First, the sampling design can bias spatial covariance parameter estimation, which in turn can bias spatial kriging estimates. Second, even with unbiased estimates of the spatial covariance parameters, since the kriging variance is a function of the observation locations, these estimates will vary based on the sample and overestimate the population-based estimates. In this work, we develop a weighted composite likelihood approach to improve spatial covariance parameter estimation under informative sampling designs. Then, given these parameter estimates, we propose three approaches to quantify the effects of the sampling design on the variance estimates in spatial prediction. These results can be used to make informed decisions for population-based inference. We illustrate our approaches using a comprehensive simulation study. Then, we apply our methods to perform spatial prediction using real estate data across a metropolitan area.

Keywords: Composite likelihood; Semivariogram estimation; Preferential sampling; Pseudo-likelihood (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10109-023-00426-9

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