A perceptron for detecting the preferential sampling of locations and times chosen to monitor a spatio-temporal process

Watson, Joe

doi:10.1016/j.spasta.2021.100500

Cited by 10 publications

(2 citation statements)

References 39 publications

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“…Whereas the sampling intensity weighted composite likelihood approach was not uniformly better than the unweighted approach, parameter estimation was improved when the sampling design was both informative and preferential. Therefore, in practice we recommend testing for preferential sampling (Schlather et al, 2004;Guan and Afshartous, 2007;Watson, 2021) prior to using this approach. Under preferential and informative sampling, combining our approaches for correcting spatial parameter estimation with the joint modeling approaches (Diggle et al, 2010;Pati et al, 2011;Gelfand et al, 2012) that account for preferential sampling for improved prediction and inference is a direction of future work.…”

Section: Discussionmentioning

confidence: 99%

Correcting spatial Gaussian process parameter and prediction variance estimation under informative sampling

Schliep,

Wikle,

Daw

2021

Preprint

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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 on nitrate concentration in wells located throughout central California.

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Section: Discussionmentioning

confidence: 99%

Correcting spatial Gaussian process parameter and prediction variance estimation under informative sampling

Schliep,

Wikle,

Daw

2021

Preprint

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“…That approach showed a more complex pattern of network modifications over time in the United Kingdom, with sequential selection favouring lower emission levels of black smoke levels in the early 1970s followed by higher emission sites being favoured in the 1980s. A simple and very general method of detecting preferential selection was subsequently developed (Watson, 2021). That method was applied in a case study of the Southern California air shed, with results being reported in a manuscript under development (Jones, Zidek & Watson, 2022).…”

Section: Preferential Samplingmentioning

confidence: 99%

Canadian contributions to environmetrics

et al. 2022

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This article focuses on the importance of collaboration in statistics by Canadian researchers and highlights the contributions that Canadian statisticians have made to many research areas in environmetrics. We provide a discussion about different vehicles that have been developed for collaboration by Canadians in the environmetrics context as well as specific scientific areas that are focused on environmetrics research in Canada including climate science, forestry, and fisheries, which are areas of importance for natural resources in Canada.Résumé: Cet article souligne l'ampleur de la collaboration entre les scientifiques canadiens dans le domaine de la statistique et présente un survol des travaux réalisés par des spécialistes du sujet sur un ensemble de thèmes liés aux sciences de l'environnement. Les auteurs relatent différents moyens par lesquels cette collaboration s'est incarnée et a permis aux Canadiennes et Canadiens de contribuer à l'environnemétrie et à divers domaines connexes, notamment les sciences du climat, la foresterie et les pêches, dont l'importance est capitale pour la gestion des ressources naturelles du pays.

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