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Found 6 papers, 1 papers with code
Joint model for longitudinal and spatio-temporal survival data
In credit risk analysis, survival models with fixed and time-varying covariates are widely used to predict a borrower's time-to-event.
The Integrated Nested Laplace Approximation for fitting Dirichlet regression models
This paper introduces a Laplace approximation to Bayesian inference in Dirichlet regression models, which can be used to analyze a set of variables on a simplex exhibiting skewness and heteroscedasticity, without having to transform the data.
Bayesian Inference
Computation
Methodology
Spatial 3D Matérn priors for fast whole-brain fMRI analysis
Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors have been shown to produce state-of-the-art activity maps without pre-smoothing the data.
Methodology Applications Computation
Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field
Estimation of stationary dependence structure parameters using only a single realisation of the spatial process, typically leads to inaccurate estimates and poorly identified parameters.
Methodology Applications
Does non-stationary spatial data always require non-stationary random fields?
A stationary spatial model is an idealization and we expect that the true dependence structures of physical phenomena are spatially varying, but how should we handle this non-stationarity in practice?
Methodology Applications
Exploring a New Class of Non-stationary Spatial Gaussian Random Fields with Varying Local Anisotropy
This allows for the introduction of parameters that control the GRF by parametrizing the diffusion matrix.
Methodology
