Optimal adaptive selection of sampling sites

Chao, Chang-Tai; Thompson, Steven K.

doi:10.1002/env.477

Cited by 22 publications

(18 citation statements)

References 14 publications

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“…It could, however, be applied adaptively, with estimates updated at each stage as information accrues. See Chao and Thompson (2001) for a discussion of optimal adaptive sampling.…”

Section: Sequential Designmentioning

confidence: 99%

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Robustness in spatial studies II: minimax design

Wiens

2005

Environmetrics

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SUMMARYWe consider robust methods for the construction of sampling designs in spatial studies. The designs are robust against misspecified regression responses, and are tailored for possible use with predictors which are minimax robust against misspecified variance/covariance structures. The loss function is based on the mean squared error of the predicted values. This is maximized, analytically, over a neighbourhood quantifying the departures from the fitted linear regression response. This maximum is then minimized numerically-by simulated annealing, or sequentially-in order to obtain the optimal designs.

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“…It could, however, be applied adaptively, with estimates updated at each stage as information accrues. See Chao and Thompson (2001) for a discussion of optimal adaptive sampling.…”

Section: Sequential Designmentioning

confidence: 99%

“…Our algorithm is a modification of that of Sacks and Schiller (1988). The Sacks and Schiller algorithm was also used, again with minor modifications, by Chao and Thompson (2001). As we apply it the algorithm depends on a sequence f j g of acceptance probabilities and parameters fn 0 ; 0 ; 1 ; ; mg, and is described as follows.…”

Section: Simulated Annealingmentioning

confidence: 99%

Robustness in spatial studies II: minimax design

Wiens

2005

Environmetrics

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“…Thompson and Seber, 1996). For example, Chao and Thompson (2001) discuss optimal adaptive selection in the spatial case, but they are not concerned with the issues of temporal dependence in spatial settings.…”

Section: Non-gaussian Space-time Dynamic Designmentioning

confidence: 97%

Dynamic design of ecological monitoring networks for non‐Gaussian spatio‐temporal data

Wikle

Royle

2005

Environmetrics

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SUMMARYMany ecological processes exhibit spatial structure that changes over time in a coherent, dynamical fashion. This dynamical component is often ignored in the design of spatial monitoring networks. Furthermore, ecological variables related to processes such as habitat are often non-Gaussian (e.g. Poisson or log-normal). We demonstrate that a simulation-based design approach can be used in settings where the data distribution is from a spatiotemporal exponential family. The key random component in the conditional mean function from this distribution is then a spatio-temporal dynamic process. Given the computational burden of estimating the expected utility of various designs in this setting, we utilize an extended Kalman filter approximation to facilitate implementation. The approach is motivated by, and demonstrated on, the problem of selecting sampling locations to estimate July brood counts in the prairie pothole region of the U.S.

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“…However, as demonstrated in Royle (1999, 2005), even more efficient designs in the spatiotemporal context can be obtained by allowing the design to change with time, where they characterize efficiency by mean squared prediction error averaged over the current spatial domain of interest. Although related to the adaptive-sampling approaches for purely spatial designs (e.g., Seber 1996, Chao andThompson 2001), these ''dynamic designs'' are fundamentally different. They explicitly account for temporal changes of the process caused by the underlying dynamics.…”

Section: Design For Data Collection In Ecological Studiesmentioning

confidence: 99%

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Cressie

Calder

Clark

et al. 2009

Ecological Applications

483

447

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Abstract. Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

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Optimal adaptive selection of sampling sites

Cited by 22 publications

References 14 publications

Robustness in spatial studies II: minimax design

Robustness in spatial studies II: minimax design

Dynamic design of ecological monitoring networks for non‐Gaussian spatio‐temporal data

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

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