155 research outputs found
Improving the INLA approach for approximate Bayesian inference for latent Gaussian models
We introduce a new copula-based correction for generalized linear mixed
models (GLMMs) within the integrated nested Laplace approximation (INLA)
approach for approximate Bayesian inference for latent Gaussian models. While
INLA is usually very accurate, some (rather extreme) cases of GLMMs with e.g.
binomial or Poisson data have been seen to be problematic. Inaccuracies can
occur when there is a very low degree of smoothing or "borrowing strength"
within the model, and we have therefore developed a correction aiming to push
the boundaries of the applicability of INLA. Our new correction has been
implemented as part of the R-INLA package, and adds only negligible
computational cost. Empirical evaluations on both real and simulated data
indicate that the method works well
A note on intrinsic Conditional Autoregressive models for disconnected graphs
In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR)
models for disconnected graphs, with the aim of providing practical guidelines
for how these models should be defined, scaled and implemented. We show how
these suggestions can be implemented in two examples on disease mapping.Comment: 14 page
A spliced Gamma-Generalized Pareto model for short-term extreme wind speed probabilistic forecasting
Renewable sources of energy such as wind power have become a sustainable
alternative to fossil fuel-based energy. However, the uncertainty and
fluctuation of the wind speed derived from its intermittent nature bring a
great threat to the wind power production stability, and to the wind turbines
themselves. Lately, much work has been done on developing models to forecast
average wind speed values, yet surprisingly little has focused on proposing
models to accurately forecast extreme wind speeds, which can damage the
turbines. In this work, we develop a flexible spliced Gamma-Generalized Pareto
model to forecast extreme and non-extreme wind speeds simultaneously. Our model
belongs to the class of latent Gaussian models, for which inference is
conveniently performed based on the integrated nested Laplace approximation
method. Considering a flexible additive regression structure, we propose two
models for the latent linear predictor to capture the spatio-temporal dynamics
of wind speeds. Our models are fast to fit and can describe both the bulk and
the tail of the wind speed distribution while producing short-term extreme and
non-extreme wind speed probabilistic forecasts.Comment: 25 page
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