Maximum likelihood estimation of the dynamic coregionalization model with heterotopic data

Fassò, Alessandro; Finazzi, Francesco

doi:10.1002/env.1123

Cited by 56 publications

(47 citation statements)

References 19 publications

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“…meteorological and geographical variables) as well as time and space dependence (e.g. Cocchi et al, 2007;Cameletti et al, 2011a;Sahu, 2012;Fassò and Finazzi, 2011). The main drawback of this formulation is related to the computational costs required for model parameter estimation and spatial prediction when MCMC methods are used, especially in case of massive spatio-temporal datasets.…”

Section: Meanmentioning

confidence: 99%

Spatial and spatio-temporal models with R-INLA

Blangiardo

Cameletti

Baio

et al. 2013

Spatial and Spatio-temporal Epidemiology

421

426

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During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constraint.Recently the use of Gaussian random fields has become increasingly popular in epidemiology as very often epidemiological data are characterised by a spatial and/or temporal structure which needs to be taken into account in the inferential process. The Integrated Nested Laplace Approximation (INLA) approach has been developed as a computationally efficient alternative to MCMC and the availability of an R package (R-INLA) allows researchers to easily apply this method.In this paper we review the INLA approach and present some applications on spatial and spatio-temporal data.

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Section: Meanmentioning

confidence: 99%

Spatial and spatio-temporal models with R-INLA

Blangiardo

Cameletti

Baio

et al. 2013

Spatial and Spatio-temporal Epidemiology

421

426

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show abstract

“…Berrocal et al, 2010b;Zidek et al, 2011). The spatio-temporal model we specify here is widely adopted in the air quality literature thanks to its flexibility in modeling relevant covariates as well as correlation in space and time (Fassò and Finazzi, 2011;Cocchi et al, 2007;Cameletti et al, 2011;Sahu, 2011). Moreover, it has been already implemented in R-INLA and validated by Cameletti et al (2013).…”

Section: First Stage: Spatio-temporal No 2 Modelmentioning

confidence: 99%

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

Blangiardo

Finazzi

Cameletti

2016

Spatial and Spatio-temporal Epidemiology

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Exposure to high levels of air pollutant concentration is known to be associated with respiratory problems which can translate into higher morbidity and mortality rates. The link between air pollution and population health has mainly been assessed considering air quality and hospitalization or mortality data. However this approach limits the analysis to individuals characterized by severe conditions. In this paper we evaluate the link between air pollution and respiratory diseases using general practice drug prescriptions for chronic respiratory diseases, which allow to draw conclusions based on the general population.We propose a two-stage statistical approach: in the first stage we specify a space-time model to estimate the monthly NO 2 concentration integrating several data sources characterized by different spatio-temporal resolution; in the second stage we link the concentration to the β 2 -agonists prescribed monthly by general practices in England and we model the prescription rates through a small area approach.

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“…The model in Equation 2 extends the model developed in Fassò and Finazzi (2011) by allowing the interaction between the latent spatial variables w j (s, t) and the loading coefficients x(s, t) and by allowing y(s, t) to be missing. The structure of the model in Equation 2 is quite general and special cases thereof have already been used.…”

Section: Model Descriptionmentioning

confidence: 99%

“…which unifies the modeling approaches developed in Zhang (2007), Fassò and Finazzi (2011) and Finazzi et al (2013).…”

Section: Model Descriptionmentioning

confidence: 99%

D-STEM: A Software for the Analysis and Mapping of Environmental Space-Time Variables

Finazzi¹,

Fassò²

2014

J. Stat. Soft.

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This paper discusses the software D-STEM as a statistical tool for the analysis and mapping of environmental space-time variables. The software is based on a flexible hierarchical space-time model which is able to deal with multiple variables, heterogeneous spatial supports, heterogeneous sampling networks and missing data. Model estimation is based on the expectation maximization algorithm and it can be performed using a distributed computing environment to reduce computing time when dealing with large data sets. The estimated model is eventually used to dynamically map the variables over the geographic region of interest. Three examples of increasing complexity illustrate usage and capabilities of D-STEM, both in terms of modeling and implementation, starting from a univariate model and arriving at a multivariate data fusion with tapering.

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Maximum likelihood estimation of the dynamic coregionalization model with heterotopic data

Cited by 56 publications

References 19 publications

Spatial and spatio-temporal models with R-INLA

Spatial and spatio-temporal models with R-INLA

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

D-STEM: A Software for the Analysis and Mapping of Environmental Space-Time Variables

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