Bayesian spatial models with a mixture neighborhood structure

Rodrigues, Érica Castilho; Assunção, Renato M.

doi:10.1016/j.jmva.2012.02.017

Cited by 35 publications

(22 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As future work, we plan to add some improvements in the BayNeRD algorithm, such as: automating both the discretization process as well as the definition of (in)dependence relationships among variables [82]; possibility to define more than two classes for the target variable; and explicit spatial influence, such as neighborhood structures [83].…”

Section: Resultsmentioning

confidence: 99%

Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations

Mello

Risso²,

Atzberger

et al. 2013

Remote Sensing

View full text Add to dashboard Cite

This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts' knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case study of soybean mapping in Mato Grosso State, Brazil, BayNeRD was tested to evaluate its capability to support the understanding of a complex phenomenon through plausible reasoning based on data observation. Observations made upon Crop Enhanced Index (CEI) values for the current and previous crop years, soil type, terrain slope, and distance to the nearest road and water body were used to calculate the probability of soybean presence for the entire Mato Grosso State, showing strong adherence to the official data. CEI values were the most influencial variables in the calculated probability of soybean presence, stating the potential of remote sensing as a OPEN ACCESSRemote Sens. 2013, 5 6000 source of data. Moreover, the overall accuracy of over 91% confirmed the high accuracy of the thematic map derived from the calculated probability values. BayNeRD allows the expert to model the relationship among several observed variables, outputs variable importance information, handles incomplete and disparate forms of data, and offers a basis for plausible reasoning from observations. The BayNeRD algorithm has been implemented in R software and can be found on the internet.

show abstract

Section: Resultsmentioning

confidence: 99%

Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations

Mello

Risso²,

Atzberger

et al. 2013

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…However, there may also be particular local variations in illness risks unrelated to those in surrounding areas, namely unstructured variation without spatial dependence. In principle, the CARðxÞ prior (also called the proper CAR prior) can represent various levels of spatial dependence through the x parameter, but this parameter does not calibrate well with marginal measures of spatial correlation, such as Moran's I (Banerjee et al 2004;Rodrigues and Assunção 2012).…”

Section: Defining Conditional Spatial Priorsmentioning

confidence: 99%

Representing spatial dependence and spatial discontinuity in ecological epidemiology: a scale mixture approach

Congdon

2016

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Variation in disease risk underlying observed disease counts is increasingly a focus for Bayesian spatial modelling, including applications in spatial data mining. Bayesian analysis of spatial data, whether for disease or other types of event, often employs a conditionally autoregressive prior, which can express spatial dependence commonly present in underlying risks or rates. Such conditionally autoregressive priors typically assume a normal density and uniform local smoothing for underlying risks. However, normality assumptions may be affected or distorted by heteroscedasticity or spatial outliers. It is also desirable that spatial disease models represent variation that is not attributable to spatial dependence. A spatial prior representing spatial heteroscedasticity within a model accommodating both spatial and non-spatial variation is therefore proposed. Illustrative applications are to human TB incidence. A simulation example is based on mainland US states, while a real data application considers TB incidence in 326 English local authorities.

show abstract

“…The traditional ICAR is defined based on adjacent neighboring areas only. Rodrigues and Assunção 14 introduced a highly parameterized approach to allow the neighbor structure to be estimated in the parameter space.…”

Section: Spatio-temporal Modeling For Case Event Datamentioning

confidence: 99%

Spatial Bayesian surveillance for small area case event data

Rotejanaprasert

Lawson

Bol

et al. 2016

Stat Methods Med Res

View full text Add to dashboard Cite

There has been little development of surveillance procedures for epidemiological data with fine spatial resolution such as case events at residential address locations. This is often due to difficulties of access when confidentiality of medical records is an issue. However, when such data are available, it is important to be able to affect an appropriate analysis strategy. We propose a model for point events in the context of prospective surveillance based on conditional logistic modeling. A weighted conditional autoregressive model is developed for irregular lattices to account for distance effects, and a Dirichlet tessellation is adopted to define the neighborhood structure. Localized clustering diagnostics are compared including the proposed local Kullback–Leibler information criterion. A simulation study is conducted to examine the surveillance and detection methods, and a data example is provided of non-Hodgkin’s lymphoma data in South Carolina.

show abstract

Bayesian spatial models with a mixture neighborhood structure

Cited by 35 publications

References 29 publications

Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations

Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations

Representing spatial dependence and spatial discontinuity in ecological epidemiology: a scale mixture approach

Spatial Bayesian surveillance for small area case event data

Contact Info

Product

Resources

About