Hierarchical generalised latent spatial quantile regression models with applications to indoor radon concentration

Fontanella, Lara; Ippoliti, Luigi; Sarra, Annalina; Valentini, Pasquale; Palermi, Sergio

doi:10.1007/s00477-014-0917-0

Cited by 14 publications

(10 citation statements)

References 33 publications

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“…Each spatial model is discussed in Appendix A.2. For instance, References [89,90] adopted ICAR and the BYM model to map the spatial pattern of tuberculosis in South Africa and malnutrition in Nigeria; Reference [91] adopted a Bayesian hierarchical spatial quantile regression model with an asymmetric Laplace spatial component to determine the risk factors of the radon-222 noble gas, which arises naturally from uranium decays; Reference [72] adopted the SPDE model to predict the spatial occurrence of fish species.…”

Section: Spatial Priorsmentioning

confidence: 99%

“…The class of others accommodates statistical models outside the above-listed classes. For instance, Reference [91] adopted a generalized hierarchical mixed model to determine the risk factors of the radon-222 noble gas; Reference [90] used a generalized hierarchical mixed model to determine the impact of carbon (IV) oxide on the prevalence of malnutrition; Reference [100] adopted a survival statistical model to map the prevalence of hospitalization due to Dengue in Wahidin Hospital in Makassar, Indonesia; Reference [52] adopted the spatial econometrics model (lag-model) to estimate the global spatial correlation of the calls to the Portuguese national health line.…”

Section: Spatial Priorsmentioning

confidence: 99%

“…The others class comprises computation techniques that do not fit into the defined classes. For instance, Reference [90] utilized the INLA interface for estimating the parameters of a Gaussian latent field model; Reference [91] utilized the MCMC approach to estimate quantile regression parameters; Reference [104] utilized the EM algorithm to perform a Bayesian interpolation of nitrogen dioxide, ozone, sulfur dioxide, and surface iron in public health units in Ontario; Reference [105] compared the performance of maximum likelihood estimation with different computation techniques for spatial scan statistics.…”

Section: Computational Techniquesmentioning

confidence: 99%

“…-Asymmetric Laplace Process Suppose Y(s) is an Asymmetric Laplace random vector with parameter p and τ, AL(p, 0, τ). According to Kuzobowski and Pogorski [85] and Fontanella et al [91] Y(s) can be written as a sum of normal and exponential process, given by,…”

Section: Appendix A4 Class Of Non-gaussian Random Fields Modelsmentioning

confidence: 99%

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Spatial Statistical Models: An Overview under the Bayesian Approach

Louzada

Nascimento

Egbon

2021

Axioms

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Spatial documentation is exponentially increasing given the availability of Big Data in the Internet of Things, enabled by device miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns in space through prior knowledge and data likelihood. However, this class of modeling is not yet well explored when compared to adopting classification and regression in machine-learning models, in which the assumption of the spatiotemporal independence of the data is often made, that is an inexistent or very weak dependence. Thus, this systematic review aims to address the main models presented in the literature over the past 20 years, identifying the gaps and research opportunities. Elements such as random fields, spatial domains, prior specification, the covariance function, and numerical approximations are discussed. This work explores the two subclasses of spatial smoothing: global and local.

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Section: Spatial Priorsmentioning

confidence: 99%

Section: Spatial Priorsmentioning

confidence: 99%

Section: Computational Techniquesmentioning

confidence: 99%

Section: Appendix A4 Class Of Non-gaussian Random Fields Modelsmentioning

confidence: 99%

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Spatial Statistical Models: An Overview under the Bayesian Approach

Louzada

Nascimento

Egbon

2021

Axioms

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show abstract

“…Salama (2005) showed that median regression analysis is more useful for detecting relationships between environmental performance and corporate financial performance than ordinary least squares regression. A hierarchical Bayesian spatial quantile regression model was proposed by Fontanella et al (2015) to analyze indoor radon concentrations. Cade and Noon (2003) provide a nice review of applications of quantile regression.…”

Section: Introductionmentioning

confidence: 99%