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Methods for preferential sampling in geostatistics
Author(s) -
Dinsdale Daniel,
SalibianBarrera Matias
Publication year - 2019
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12286
Subject(s) - monte carlo method , geostatistics , sampling (signal processing) , statistics , function (biology) , importance sampling , set (abstract data type) , kriging , statistical physics , mathematics , computer science , econometrics , algorithm , spatial variability , physics , filter (signal processing) , evolutionary biology , computer vision , biology , programming language
Summary Preferential sampling in geostatistics occurs when the locations at which observations are made may depend on the spatial process that underlines the correlation structure of the measurements. We show that previously proposed Monte Carlo estimates for the likelihood function may not be approximating the desired function. Furthermore, we argue that, for preferential sampling of moderate complexity, alternative and widely available numerical methods to approximate the likelihood function produce better results than Monte Carlo methods. We illustrate our findings on the Galicia data set analysed previously in the literature.