Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Rue, H��vard"'
Statistical analysis based on quantile regression methods is more comprehensive, flexible, and less sensitive to outliers when compared to mean regression methods. When the link between different diseases are of interest, joint disease mapping is use
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ddb8a006a0f108a6ad6e2d5d6ac66784
http://arxiv.org/abs/2201.12902
http://arxiv.org/abs/2201.12902
The popular Bayesian meta-analysis expressed by Bayesian normal-normal hierarchical model (NNHM) synthesizes knowledge from several studies and is highly relevant in practice. Moreover, NNHM is the simplest Bayesian hierarchical model (BHM), which il
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4f67f7e4e9beb53acaab8d3ab5ef494
http://arxiv.org/abs/2109.11870
http://arxiv.org/abs/2109.11870
This paper presents the development of a spatial block-Nearest Neighbor Gaussian process (block-NNGP) for location-referenced large spatial data. The key idea behind this approach is to divide the spatial domain into several blocks which are dependen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b400ee320ce2f9d7d256bfffb5268a71
http://arxiv.org/abs/1908.06437
http://arxiv.org/abs/1908.06437
Autor:
Bakka, Haakon, Rue, H��vard, Fuglstad, Geir-Arne, Riebler, Andrea, Bolin, David, Krainski, Elias, Simpson, Daniel, Lindgren, Finn
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically-sized datasets from scratch is time-consuming, and if changes a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::749fb37cb6ba6e9fe15dd2d6639ec3a3
http://arxiv.org/abs/1802.06350
http://arxiv.org/abs/1802.06350
Autor:
S��rbye, Sigrunn Holbek, Rue, H��vard
Fractional Gaussian noise (fGn) is a self-similar stochastic process used to model anti-persistent or persistent dependency structures in observed time series. Properties of the autocovariance function of fGn are characterised by the Hurst exponent (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f7fd602858b0650dacfbde17af2569f7
Autor:
S��rbye, Sigrunn Holbek, Rue, H��vard
The autoregressive process of order $p$ (AR($p$)) is a central model in time series analysis. A Bayesian approach requires the user to define a prior distribution for the coefficients of the AR($p$) model. Although it is easy to write down some prior
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0cfcd0c9cf9c08359318ee36d67c7464
In a bivariate meta-analysis the number of diagnostic studies involved is often very low so that frequentist methods may result in problems. Bayesian inference is attractive as informative priors that add small amount of information can stabilise the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6dee22a05406f4d87fa125e56a207508
http://arxiv.org/abs/1512.06217
http://arxiv.org/abs/1512.06217
Autor:
Ferkingstad, Egil, Rue, H��vard
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 accura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9bd0ee3bac2a5ce9b143365e02df166
In this paper a new approach for constructing \emph{multivariate} Gaussian random fields (GRFs) using systems of stochastic partial differential equations (SPDEs) has been introduced and applied to simulated data and real data. By solving a system of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd63ba9852c8ffd4b72bd99c9accfcb4
http://arxiv.org/abs/1307.1379
http://arxiv.org/abs/1307.1379
A non-stationary spatial Gaussian random field (GRF) is described as the solution of an inhomogeneous stochastic partial differential equation (SPDE), where the covariance structure of the GRF is controlled by the coefficients in the SPDE. This allow
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03453ec91396d3cc88d9b247e86c0ff9
http://arxiv.org/abs/1306.0408
http://arxiv.org/abs/1306.0408