Non‐Gaussian geostatistical modeling using (skew) t processes

Bevilacqua, Moreno; Caamaño‐Carrillo, Christian; Arellano–Valle, Reinaldo B.; Morales-Oñate, Víctor

doi:10.1111/sjos.12447

Cited by 25 publications

(26 citation statements)

References 73 publications

(83 reference statements)

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“…The blockwise approach guarantees considerable computational gains over the standard pairwise composite likelihood method and our implementation in OpenCL allows us to obtain further improvements in the computation of the estimates. Although in this paper we only considered spatio-temporal Gaussian random fields, the proposed methodology can be easily extended to the case of the estimation of spatio-temporal non-Gaussian random fields with known bivariate distribution as, for example, in Alegría et al (2017) and Bevilacqua et al (2020).…”

Section: Discussionmentioning

confidence: 99%

“…Gaussian random fields (RFs) are the cornerstone for this kind of analysis and have been largely used in the past years thanks to a well developed and rich theory. Moreover, they represent the building block for more sophisticated models or non-Gaussian RFs (see, for instance, De Oliveira et al (1997), Xu & Genton (2017) and Bevilacqua et al (2020)).…”

Section: Introductionmentioning

confidence: 99%

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Blockwise Euclidean likelihood for spatio-temporal covariance models

Morales-Oñate

Crudu

Bevilacqua

2021

Econometrics and Statistics

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In this paper we propose a spatio-temporal blockwise Euclidean likelihood method for the estimation of covariance models when dealing with large spatio-temporal Gaussian data. The method uses moment conditions coming from the score of the pairwise composite likelihood. The blockwise approach guarantees considerable computational improvements over the standard pairwise composite likelihood method. In order to further speed up computation we consider a general purpose graphics processing unit implementation using OpenCL. We derive the asymptotic properties of the proposed estimator and we illustrate the finite sample properties of our methodology by means of a simulation study highlighting the computational gains of the OpenCL graphics processing unit implementation. Finally, we apply our estimation method to a wind component data set.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blockwise Euclidean likelihood for spatio-temporal covariance models

Morales-Oñate

Crudu

Bevilacqua

2021

Econometrics and Statistics

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“…To account for outliers Baingana et al (2015) propose an estimator to robustify the kriged Kalman filter, extending the spatio-temporal approach of Mardia et al (1998) which is highly affected by outlying observations. Bevilacqua et al (2020) propose a skew-t model for geostatistical data aiming to accommodate fat tails and asymmetric marginal distributions.…”

Section: Related Literaturementioning

confidence: 99%

Dynamical non-Gaussian modelling of spatial processes

Fonseca¹,

R.²,

Schmidt³

2021

Preprint

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Spatio-temporal processes in environmental applications are often assumed to follow a Gaussian model, possibly after some transformation. However, heterogeneity in space and time might have a pattern that will not be accommodated by transforming the data. In this scenario, modelling the variance laws is an appealing alternative. This work adds flexibility to the usual Multivariate Dynamic Gaussian model by defining the process as a scale mixture between a Gaussian and log-Gaussian processes. The scale is represented by a process varying smoothly over space and time which is allowed to depend on covariates. State-space equations define the dynamics over time for both mean and variance processes resulting in feasible inference and prediction. Analysis of artificial datasets show that the parameters are identifiable and simpler models are well recovered by the general proposed model. The analyses of two important environmental processes, maximum temperature and maximum ozone, illustrate the effectiveness of our proposal in improving the uncertainty quantification in the prediction of spatio-temporal processes.

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“…Tagle, Castruccio, Crippa, and Genton () considered independent multivariate ST distributions in a regional gridded setting. More recently, Bevilacqua, Caamaño‐Carrillo, Arellano‐Valle, and Morales‐Onñate () extended the work of Zhang and El‐Shaarawi (), by adding scale mixing in the form of a gamma process, thus achieving ST marginals.…”

Section: A New Spatial Model For Skewed Datamentioning

confidence: 99%

A high‐resolution bilevel skew‐tstochastic generator for assessing Saudi Arabia's wind energy resources

et al. 2020

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Saudi Arabia has recently established its renewable energy targets as part of its "Vision 2030" proposal, which represents a roadmap for reducing the country's dependence on oil over the next decade. This study provides a foundational assessment of the wind resource in Saudi Arabia that serves as a guide for the development of the outlined wind energy component. The assessment is based on a new high-resolution weather simulation of the region generated with the Weather Research and Forecasting (WRF) model. Furthermore, we propose a spatiotemporal stochastic generator of daily wind speeds that assists in characterizing the uncertainty of the energy estimates. The stochastic generator considers a vector autoregressive structure in time, with innovations from a novel biresolution model based on a skew-t distribution with a low-dimensional latent structure. Estimation of the spatial model parameters is performed using a Monte Carlo expectation-maximization (EM) algorithm, which achieves inference over approximately 184 million points and enables to capture the spatial patterns of the higher order moments that typically characterize high-resolution wind fields. Our results identify regions along the western mountain ranges and central escarpments that are suitable for the deployment of wind energy infrastructure. According to the assessment, between 30 and 70% of the national electricity demand could be met by wind energy.

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Non‐Gaussian geostatistical modeling using (skew) t processes

Cited by 25 publications

References 73 publications

Blockwise Euclidean likelihood for spatio-temporal covariance models

Blockwise Euclidean likelihood for spatio-temporal covariance models

Dynamical non-Gaussian modelling of spatial processes

A high‐resolution bilevel skew‐tstochastic generator for assessing Saudi Arabia's wind energy resources

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