Adaptive Gaussian predictive process models for large spatial datasets

Guhaniyogi, Rajarshi; Finley, Andrew O.; Banerjee, Sudipto; Gelfand, Alan E.

doi:10.1002/env.1131

Cited by 52 publications

(42 citation statements)

References 23 publications

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“…For random knot selection, Guhaniyogi et al (2011) introduced an adaptive predictive process model for spatial data. They fixed the knot number and modeled knot locations with a point pattern model.…”

Section: Selection Of Tuning Parametersmentioning

confidence: 99%

Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

Zhang¹,

Sang²,

Huang³

2014

STAT SINICA

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. (2015). Full-scale approximations of spatio-temporal covariance models for large datasets. Statistica Sinica, 25 99-114. Full-scale approximations of spatio-temporal covariance models for large datasets AbstractVarious continuously-indexed spatio-temporal process models have been constructed to characterize spatiotemporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatiotemporal context to reduce computational complexity. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points. Our approach is applicable to nonseparable and nonstationary spatio-temporal covariance models. We illustrate the effectiveness of our method through simulation experiments and application to an ozone measurement dataset. Disciplines Engineering | Science and Technology Studies FULL-SCALE APPROXIMATIONS OF SPATIO-TEMPORAL COVARIANCE MODELS FOR LARGE DATASETSBohai Zhang, Huiyan Sang and Jianhua Z. Huang Texas A&M UniversityAbstract: Various continuously-indexed spatio-temporal process models have been constructed to characterize spatio-temporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points. Our approach is applicable to nonseparable and nonstationary spatio-temporal covariance models. We illustrate the effectiveness of our method through simulation experiments and application to an ozone measurement dataset.

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Section: Selection Of Tuning Parametersmentioning

confidence: 99%

Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

Zhang¹,

Sang²,

Huang³

2014

STAT SINICA

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“…Conditional on a fixed number of knots, Gelfand et al (2012) discussed an approach to place them optimally using a minimum predictive variance criterion. A modelbased approach for random knots was introduced in Guhaniyogi et al (2011). There, in a multi-stage structure, a point process prior was assumed for the set of knots.…”

Section: Hierarchical Spatial Model For Rainratementioning

confidence: 99%

“…This approximation is coherent with the MARS model introduced in Section 3, where the resulting covariance was shown to depend on a set of random sp-knots. We like to mention that, conditional on a fixed number of pp-knots m, we can allow their locations to vary by using a point process prior on them as in Guhaniyogi et al (2011). However, the hierarchical model described in Section 3 already includes an adaptive spatial function based on sp-knots and GP is used only as a model for the residual process.…”

Section: Knot-based Approximation For Large Datasetmentioning

confidence: 99%

An adaptive spatial model for precipitation data from multiple satellites over large regions

Chakraborty

De²,

Bowman

et al. 2013

Stat Comput

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Satellite measurements have of late become an important source of information for climate features such as precipitation due to their near-global coverage. In this article, we look at a precipitation dataset during a 3-hour window over tropical South America that has information from two satellites. We develop a flexible hierarchical model to combine instantaneous rainrate measurements from those satellites while accounting for their potential heterogeneity. Conceptually, we envision an underlying precipitation surface that influences the observed rain as well as absence of it. The surface is specified using a mean function centered at a set of knot locations, to capture the local patterns in the rainrate, combined with a residual Gaussian process to account for global correlation across sites. To improve over the commonly used pre-fixed knot choices, an efficient reversible jump scheme is used to allow the number of such knots as well as the order and support of associated polynomial terms to be chosen adaptively.To facilitate computation over a large region, a reduced rank approximation for the parent Gaussian process is employed.

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“…The advent of machine learning is removing the second barrier at a time when the availability of "Big Data" is ascendant in all fields [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Holistics 3.0 for Health

Lary

Woolf

Faruque

et al. 2014

IJGI

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Human health is part of an interdependent multifaceted system. More than ever, we have increasingly large amounts of data on the body, both spatial and non-spatial, its systems, disease and our social and physical environment. These data have a geospatial component. An exciting new era is dawning where we are simultaneously collecting multiple datasets to describe many aspects of health, wellness, human activity, environment and disease. Valuable insights from these datasets can be extracted using massively multivariate computational techniques, such as machine learning, coupled with geospatial techniques. These computational tools help us to understand the topology of the data and provide insights for scientific discovery, decision support and policy formulation. This paper outlines a holistic paradigm called Holistics 3.0 for analyzing health data with a set of examples. Holistics 3.0 combines multiple big datasets set in their geospatial context describing as many areas of a problem as possible with machine learning and causality, to both learn from the data and to construct tools for data-driven decisions.

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Adaptive Gaussian predictive process models for large spatial datasets

Cited by 52 publications

References 23 publications

Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

An adaptive spatial model for precipitation data from multiple satellites over large regions

Holistics 3.0 for Health

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