Comments on: Spatiotemporal models for skewed processes

Genton, Marc G.; Hering, Amanda S.

doi:10.1002/env.2430

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…The basic component Z(s) is then supplemented by additional terms to regulate location and scale. An extension of this model to accommodate observed temporal replicates at each geographical location, illustrated by applied work, has been presented by Schmidt et al [98]; see also the subsequent comments by Genton and Hering [99]. [106], and Boojari et al [107], among others.…”

Section: Spatial and Spatio-temporal Modelsmentioning

confidence: 99%

A Selective Overview of Skew-Elliptical and Related Distributions and of Their Applications

Adcock

Azzalini

2020

Symmetry

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Within the context of flexible parametric families of distributions, much work has been dedicated in recent years to the theme of skew-symmetric distributions, or symmetry-modulated distributions, as we prefer to call them. The present contribution constitutes a review of this area, with special emphasis on multivariate skew-elliptical families, which represent the subset with more immediate impact on applications. After providing background information of the distribution theory aspects, we focus on the aspects more relevant for applied work. The exposition is targeted to non-specialists in this domain, although some general knowledge of probability and multivariate statistics is assumed. Given this aim, the mathematical profile is kept to the minimum required.

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Section: Spatial and Spatio-temporal Modelsmentioning

confidence: 99%

A Selective Overview of Skew-Elliptical and Related Distributions and of Their Applications

Adcock

Azzalini

2020

Symmetry

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“…Strictly speaking, Schmidt et al (2017) presented an extension of the spatial skewed model proposed by Zhang and El-Shaarawi (2010) for the spatio-temporal data. However, several questions with regard to this model were posed by Genton and Hering (2017). Besides, the finite-dimensional distributions of the process do not belong to any of the commonly considered families of the multivariate skew-normal distributions.…”

Section: Introductionmentioning

confidence: 99%

A Skew-Gaussian‎ ‎Spatio-Temporal Process with Non-Stationary Correlation Structure

Barzegar

Rivaz

Khaledi

2019

JIRSS

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This paper develops a new class of spatio-temporal process models that can simultaneously capture skewness and non-stationarity. The proposed approach which is based on using the closed skew-normal distribution in the low-rank representation of stochastic processes, has several favorable properties. In particular, it greatly reduces the dimension of the spatio-temporal latent variables and induces flexible correlation structures. Bayesian analysis of the model is implemented through a Gibbs MCMC algorithm which incorporates a version of the Kalman filtering algorithm. All fully conditional posterior distributions have closed forms which show another advantageous property of the proposed model. We demonstrate the efficiency of our model through an extensive simulation study and an application to a real data set comprised of precipitation measurements.

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“…Their model is based on the combination of Gaussian processes with purely spatial dependence structures and a purely temporal component. The joint density in that model is not possible to obtain in a simple form and it cannot handle data with tail dependence; see also the discussion by Genton and Hering (2017).…”

Section: Introductionmentioning

confidence: 99%

Factor copula models for data with spatio-temporal dependence

Krupskii

Genton

2017

Spatial Statistics

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We propose a new copula model for spatial data that are observed repeatedly in time. The model is based on the assumption that there exists a common factor that affects the measurements of a process in space and in time. Unlike models based on multivariate normality, our model can handle data with tail dependence and asymmetry. The likelihood for the proposed model can be obtained in a simple form and therefore parameter estimation is quite fast. Simulation from this model is straightforward and data can be predicted at any spatial location and time point. We use simulation studies to show different types of dependencies, both in space and in time, that can be generated by this model. We apply the proposed copula model to hourly wind data and compare its performance with some classical models for spatio-temporal data.

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Comments on: Spatiotemporal models for skewed processes

Cited by 4 publications

References 14 publications

A Selective Overview of Skew-Elliptical and Related Distributions and of Their Applications

A Selective Overview of Skew-Elliptical and Related Distributions and of Their Applications

A Skew-Gaussian‎ ‎Spatio-Temporal Process with Non-Stationary Correlation Structure

Factor copula models for data with spatio-temporal dependence

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