Bayesian Prediction of Transformed Gaussian Random Fields

Oliveira, Victor De; Kedem, Benjamin; Short, David

doi:10.1080/01621459.1997.10473663

Cited by 144 publications

(97 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In some cases, such mapping is one-to-one and admits an inverse simplifying the analysis. In this class, we can highlight the log-Gaussian RFs, which are generated as a particular example of the Box-Cox transformation (see De Oliveira et al, 1997). However, a one-to-one transformation is not appropriate in general.…”

Section: Introductionmentioning

confidence: 99%

Estimating covariance functions of multivariate skew-Gaussian random fields on the sphere

et al. 2017

View full text Add to dashboard Cite

This paper considers a multivariate spatial random field, with each component having univariate marginal distributions of the skew-Gaussian type. We assume that the field is defined spatially on the unit sphere embedded in R 3 , allowing for modeling data available over large portions of planet Earth. This model admits explicit expressions for the marginal and cross covariances. However, the n-dimensional distributions of the field are difficult to evaluate, because it requires the sum of 2 n terms involving the cumulative and probability density functions of a n-dimensional Gaussian distribution. Since in this case inference based on the full likelihood is computationally unfeasible, we propose a composite likelihood approach based on pairs of spatial observations. This last being possible thanks to the fact that we have a closed form expression for the bivariate distribution. We illustrate the effectiveness of the method through simulation experiments and the analysis of a real data set of minimum and maximum surface air temperatures.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimating covariance functions of multivariate skew-Gaussian random fields on the sphere

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The main approaches that have been considered to account for parameter uncertainty when performing predictive inference about Z ( s 0 ) include the Bayesian approach (Handcock and Stein, 1993; De Oliveira, Kedem and Short, 1997) and bootstrap approaches (Sjöstedt-de Luna and Young, 2003; Wang and Wall, 2003; De Oliveira and Rui, 2009; Schelin and Sjöstedt-de Luna, 2010). …”

Section: Calibrated Prediction Intervalsmentioning

confidence: 99%

Prediction intervals for integrals of Gaussian random fields

Oliveira¹,

Kone²

2015

Computational Statistics & Data Analysis

Self Cite

View full text Add to dashboard Cite

Methodology is proposed for the construction of prediction intervals for integrals of Gaussian random fields over bounded regions (called block averages in the geostatistical literature) based on observations at a finite set of sampling locations. Two bootstrap calibration algorithms are proposed, termed indirect and direct, aimed at improving upon plug-in prediction intervals in terms of coverage probability. A simulation study is carried out that illustrates the effectiveness of both procedures, and these procedures are applied to estimate block averages of chromium traces in a potentially contaminated region in Switzerland.

show abstract

“…Thus, spatial data correlation can be formed between source event and the observation of each sensor node within its sensing range. Spatial data correlation among sensor nodes within spherical range of a point source event can be modeled by covariance models [5], [6], [7] described as follows;…”

Section: Three Dimensional Event Based Spatially Correlated Clustmentioning

confidence: 99%

Spatial data estimation in three dimensional distributed wireless sensor networks

Karjee

Kleasinsteuber

Jamadagni³

2014

2014 International Conference on Embedded Systems (ICES)

View full text Add to dashboard Cite

Due to deployment of inflated amount of sensor nodes in three dimensional space, observed data are highly correlated among sensor nodes. Since the data are highly correlated, it produces large quantity of redundant data in the network. To reduce data redundancy, we propose a clustering algorithm called Three Dimensional Event based Spatially Correlated Clustering (3D-ESCC) algorithm. Moreover, to extract more accurate data in each distributed cluster of 3D-ESCC algorithm, we propose an Event based Data Estimation (EDE) model in three dimensional space and compare it with other data estimation models. In distributed wireless sensor networks, it may be possible that due to extreme physical condition (e.g heavy rainfall, high temperature and battery discharge) the sensor nodes fails to operate. In such situation, we are able to develop a data prediction model in distributed cluster in case of node failure. Computer simulations and validations are performed to validate 3D-ESCC algorithm and EDE model.

show abstract

Bayesian Prediction of Transformed Gaussian Random Fields

Cited by 144 publications

References 18 publications

Estimating covariance functions of multivariate skew-Gaussian random fields on the sphere

Estimating covariance functions of multivariate skew-Gaussian random fields on the sphere

Prediction intervals for integrals of Gaussian random fields

Spatial data estimation in three dimensional distributed wireless sensor networks

Contact Info

Product

Resources

About