title: Preferential sampling and Bayesian geostatistics: Statistical modeling and examples.
abstract: Preferential sampling refers to any situation in which the spatial process and the sampling locations are not stochastically independent. In this paper, we present two examples of geostatistical analysis in which the usual assumption of stochastic independence between the point process and the measurement process is violated. To account for preferential sampling, we specify a flexible and general Bayesian geostatistical model that includes a shared spatial random component. We apply the proposed model to two different case studies that allow us to highlight three different modeling and inferential aspects of geostatistical modeling under preferential sampling: (1) continuous or finite spatial sampling frame; (2) underlying causal model and relevant covariates; and (3) inferential goals related to mean prediction surface or prediction uncertainty.
authors: Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Italy cecconi@disia.unifi.it.
authors: Lorenzo
authors: L
authors: Cecconi
authors: Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Italy.
authors: Laura
authors: L
authors: Grisotto
authors: Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Italy Biostatistics Unit, Institute for Cancer Prevention and Research, Tuscany Region, Italy.
authors: Dolores
authors: D
authors: Catelan
authors: Department of Economics, University of Genoa, Italy.
authors: Corrado
authors: C
authors: Lagazio
authors: Department of Biostatistics, University of Michigan, USA.
authors: Veronica
authors: V
authors: Berrocal
authors: Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Italy Biostatistics Unit, Institute for Cancer Prevention and Research, Tuscany Region, Italy.
authors: Annibale
authors: A
authors: Biggeri
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D062305:Geographic Mapping; D007558:Italy / Q000453:epidemiology; D015233:Models, Statistical; D015995:Prevalence; D012756:Sheep / Q000469:parasitology; D012757:Sheep Diseases / Q000453:epidemiology / Q000469:parasitology; D013269:Stochastic Processes; D035501:Uncertainty
keywords: Bayesian geostatistics; Preferential sampling; shared component model