Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Geostatistical inference under preferential sampling
AU - Diggle, Peter J.
AU - Menezes, Raquel
AU - Su, Ting-li
PY - 2010/3
Y1 - 2010/3
N2 - Geostatistics involves the fitting of spatially continuous models to spatially discrete data. Preferential sampling arises when the process that determines the data locations and the process being modelled are stochastically dependent. Conventional geostatistical methods assume, if only implicitly, that sampling is non-preferential. However, these methods are often used in situations where sampling is likely to be preferential. For example, in mineral exploration, samples may be concentrated in areas that are thought likely to yield high grade ore. We give a general expression for the likelihood function of preferentially sampled geostatistical data and describe how this can be evaluated approximately by using Monte Carlo methods. We present a model for preferential sampling and demonstrate through simulated examples that ignoring preferential sampling can lead to misleading inferences. We describe an application of the model to a set of biomonitoring data from Galicia, northern Spain, in which making allowance for preferential sampling materially changes the results of the analysis.
AB - Geostatistics involves the fitting of spatially continuous models to spatially discrete data. Preferential sampling arises when the process that determines the data locations and the process being modelled are stochastically dependent. Conventional geostatistical methods assume, if only implicitly, that sampling is non-preferential. However, these methods are often used in situations where sampling is likely to be preferential. For example, in mineral exploration, samples may be concentrated in areas that are thought likely to yield high grade ore. We give a general expression for the likelihood function of preferentially sampled geostatistical data and describe how this can be evaluated approximately by using Monte Carlo methods. We present a model for preferential sampling and demonstrate through simulated examples that ignoring preferential sampling can lead to misleading inferences. We describe an application of the model to a set of biomonitoring data from Galicia, northern Spain, in which making allowance for preferential sampling materially changes the results of the analysis.
KW - Environmental monitoring
KW - Geostatistics
KW - Log-Gaussian Cox process
KW - Marked point process
KW - Monte Carlo inference
KW - Preferential sampling
KW - MARKED POINT-PROCESSES
KW - DEPENDENT FOLLOW-UP
KW - AIR-POLLUTION
KW - POISSON INTENSITY
KW - LONGITUDINAL DATA
KW - COX PROCESSES
KW - VARIOGRAM
KW - MODEL
KW - DESIGN
KW - TEMPERATURE
UR - http://www.scopus.com/inward/record.url?scp=77949520299&partnerID=8YFLogxK
U2 - 10.1111/j.1467-9876.2009.00701.x
DO - 10.1111/j.1467-9876.2009.00701.x
M3 - Journal article
VL - 59
SP - 191
EP - 232
JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)
JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)
SN - 0035-9254
IS - 2
ER -