Spatial and spatio-temporal models with R-INLA
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TLDR
The Integrated Nested Laplace Approximation 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.About:
This article is published in Spatial and Spatio-temporal Epidemiology.The article was published on 2013-03-01 and is currently open access. It has received 396 citations till now. The article focuses on the topics: Markov chain Monte Carlo.read more
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Efficient and Flexible Integration of Variant Characteristics in Rare Variant Association Studies Using Integrated Nested Laplace Approximation
Hana Susak,Laura Serra-Saurina,Raquel Rabionet Janssen,Laura Domènech,Mattia Bosio,Francesc Muyas,Xavier Estivill,Geòrgia Escaramís,Stephan Ossowsky +8 more
TL;DR: A novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI), which demonstrates that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation.
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A Linear Mixed Model Formulation for Spatio-Temporal Random Processes with Computational Advances for the Separable and Product-Sum Covariances
TL;DR: The proposed linear mixed model formulation facilitates the implementation of a novel algorithm using Stegle eigendecompositions, a recursive application of the Sherman-Morrison-Woodbury formula, and Helmert-Wolf blocking to efficiently invert separable and product-sum covariance matrices.
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A hierarchical mixed effect hurdle model for spatiotemporal count data and its application to identifying factors impacting health professional shortages
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Model-Based Geostatistics from a Bayesian Perspective: Investigating Area-to-Point Kriging with Small Data Sets
TL;DR: The prior distribution is found to have minimal impact on the disaggregated predictions, and some severe effects of model misspecification in terms of overly conservative or optimistic prediction uncertainties are found, highlighting the importance of model choice or integration into ATPK.
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A continuous spatio-temporal model for house prices in the USA
TL;DR: In this article, the authors revisited the studies on the evolution of house prices in the USA using a spatio-temporal model estimated using a Bayesian method and introduced a new specification of an error correction model with random effects measured continuously in space.
References
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Journal ArticleDOI
Bayesian measures of model complexity and fit
TL;DR: In this paper, the authors consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined and derive a measure pD for the effective number in a model as the difference between the posterior mean of the deviances and the deviance at the posterior means of the parameters of interest, which is related to other information criteria and has an approximate decision theoretic justification.
Book
Statistics for spatial data
Noel A Cressie,Noel A Cressie +1 more
TL;DR: In this paper, the authors present a survey of statistics for spatial data in the field of geostatistics, including spatial point patterns and point patterns modeling objects, using Lattice Data and spatial models on lattices.
Book
Monte Carlo Statistical Methods
TL;DR: This new edition contains five completely new chapters covering new developments and has sold 4300 copies worldwide of the first edition (1999).
Journal ArticleDOI
5. Statistics for Spatial Data
Mike Rees,N. Cressie +1 more
TL;DR: Cressie et al. as discussed by the authors presented the Statistics for Spatial Data (SDS) for the first time in 1991, and used it for the purpose of statistical analysis of spatial data.