Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification

Hanks, Ephraim M.; Schliep, Erin M.; Hooten, Mevin B.; Hoeting, Jennifer A.

doi:10.1002/env.2331

Cited by 140 publications

(182 citation statements)

References 30 publications

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“…Murakami and Griffith () demonstrated that, in the presence of such spatial process, the use of E ⊥ leads to a severe overestimation of the statistical significance of the coefficients. Hanks, Schliep, Hooten, and Hoeting () obtained similar results through simulations under misspecification.…”

Section: Spatial Extension Of the Uqrmentioning

confidence: 59%

Spatially filtered unconditional quantile regression: Application to a hedonic analysis

Murakami

Seya

2019

Environmetrics

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Unconditional quantile regression (UQR) attracts attention in various fields to investigate the impacts of explanatory variables on quantiles of the marginal distribution of an explained variable. This study attempts to introduce spatial dependence into the UQR within the framework of random effects eigenvector spatial filtering, resulting in the model that we term the spatially filtered UQR (SF‐UQR). We then develop a computationally efficient approach for SF‐UQR estimation. Finally, the performance of the SF‐UQR is tested with a hedonic land price model for the Tokyo metropolitan area. SF‐UQR is implemented in an R package, “spmoran.”

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Section: Spatial Extension Of the Uqrmentioning

confidence: 59%

Spatially filtered unconditional quantile regression: Application to a hedonic analysis

Murakami

Seya

2019

Environmetrics

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“…() and Hanks et al. (). Heuristically, our Kα serves as an autocovariate relating a site to the occupancy status of its neighbors.…”

Section: Methodsmentioning

confidence: 99%

Dynamic occupancy models for explicit colonization processes

Broms

Hooten

Johnson

et al. 2016

Ecology

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The dynamic, multi‐season occupancy model framework has become a popular tool for modeling open populations with occupancies that change over time through local colonizations and extinctions. However, few versions of the model relate these probabilities to the occupancies of neighboring sites or patches. We present a modeling framework that incorporates this information and is capable of describing a wide variety of spatiotemporal colonization and extinction processes. A key feature of the model is that it is based on a simple set of small‐scale rules describing how the process evolves. The result is a dynamic process that can account for complicated large‐scale features. In our model, a site is more likely to be colonized if more of its neighbors were previously occupied and if it provides more appealing environmental characteristics than its neighboring sites. Additionally, a site without occupied neighbors may also become colonized through the inclusion of a long‐distance dispersal process. Although similar model specifications have been developed for epidemiological applications, ours formally accounts for detectability using the well‐known occupancy modeling framework. After demonstrating the viability and potential of this new form of dynamic occupancy model in a simulation study, we use it to obtain inference for the ongoing Common Myna (Acridotheres tristis) invasion in South Africa. Our results suggest that the Common Myna continues to enlarge its distribution and its spread via short distance movement, rather than long‐distance dispersal. Overall, this new modeling framework provides a powerful tool for managers examining the drivers of colonization including short‐ vs. long‐distance dispersal, habitat quality, and distance from source populations.

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“…Spatial confounding and related identifiability issues are also challenging topics in spatial models (Hodges and Reich 2010). Spatial confounding has primarily been discussed for areal spatial data (e.g., Paciorek 2010), however, recently (Hanks et al 2015) studied spatial confounding for geostatistical H processes (i.e., continuous spatial support). Multivariate spatial and spatiotemporal models as well as nonlinear dynamical spatiotemporal models (e.g., Wikle and Hooten 2010) are active areas of research.…”

Section: Future Directionsmentioning

confidence: 99%

“…Traditional likelihoodbased approaches to modeling have allowed for scientifically meaningful data structures, though, in complicated situations with heavily parameterized models and limited or missing data; estimation by likelihood maximization is often problematic or infeasible. Developments in numerical approximation methods have been useful in many cases, especially for highdimensional parameter spaces (e.g., NewtonRaphson and E-M methods, Givens and Hoeting 2005), though can still be difficult or impossible to implement and have no provision for accommodating uncertainty at multiple levels.…”

Section: Hierarchical Modelsmentioning

confidence: 99%

Hierarchical Spatial Models

Arab¹,

Hooten²,

Wikle³

2015

Encyclopedia of GIS

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SynonymsHierarchical dynamic spatiotemporal models; Geostatistical models Definition A hierarchical spatial model is the product of conditional distributions for data conditioned on a spatial process and parameters, the spatial process conditioned on the parameters defining the spatial dependencies between process locations and the parameters themselves. Historical BackgroundScientists across a wide range of disciplines have long recognized the importance of spatial dependencies in their data and the underlying process of interest. Initially due to computational limitations, they dealt with such dependencies by randomization and blocking rather than the explicit characterization of the dependencies in their models. Early developments in spatial modeling started in the 1950s and 1960s motivated by problems in mining engineering and meteorology (Cressie 1993), followed by the introduction of Markov random fields (Besag 1974). The application of hierarchical spatial and spatiotemporal models have become increasingly popular since the advancements of computational techniques, such as MCMC methods, in the later years of the twentieth century. Scientific FundamentalsMethods for spatial and spatiotemporal modeling are becoming increasingly important in the environmental sciences and other sciences where data arise from processes in spatial settings. Unfortunately, the application of traditional covariance-based spatial statistical models is either inappropriate or computationally

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Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification

Cited by 140 publications

References 30 publications

Spatially filtered unconditional quantile regression: Application to a hedonic analysis

Spatially filtered unconditional quantile regression: Application to a hedonic analysis

Dynamic occupancy models for explicit colonization processes

Hierarchical Spatial Models

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