Zobrazeno 1 - 10
of 338
pro vyhledávání: '"Bolin, David"'
The computational cost for inference and prediction of statistical models based on Gaussian processes with Mat\'ern covariance functions scales cubicly with the number of observations, limiting their applicability to large data sets. The cost can be
Externí odkaz:
http://arxiv.org/abs/2410.13000
Scoring rules are aimed at evaluation of the quality of predictions, but can also be used for estimation of parameters in statistical models. We propose estimating parameters of multivariate spatial models by maximising the average leave-one-out cros
Externí odkaz:
http://arxiv.org/abs/2408.11994
In econometrics, the Efficient Market Hypothesis posits that asset prices reflect all available information in the market. Several empirical investigations show that market efficiency drops when it undergoes extreme events. Many models for multivaria
Externí odkaz:
http://arxiv.org/abs/2408.06661
Autor:
Bolin, David, Wallin, Jonas
The estimation of regression parameters in spatially referenced data plays a crucial role across various scientific domains. A common approach involves employing an additive regression model to capture the relationship between observations and covari
Externí odkaz:
http://arxiv.org/abs/2403.18961
Penalizing complexity (PC) priors is a principled framework for designing priors that reduce model complexity. PC priors penalize the Kullback-Leibler Divergence (KLD) between the distributions induced by a ``simple'' model and that of a more complex
Externí odkaz:
http://arxiv.org/abs/2312.04481
Autor:
Chaudhuri, Somnath, Barceló, Maria A., Juan, Pablo, Varga, Diego, Bolin, David, Rue, Haavard, Saez, Marc
Spatial statistics is traditionally based on stationary models on $\mathbb{R^d}$ like Mat\'ern fields. The adaptation of traditional spatial statistical methods, originally designed for stationary models in Euclidean spaces, to effectively model phen
Externí odkaz:
http://arxiv.org/abs/2312.01166
Brain functional connectivity (FC), the temporal synchrony between brain networks, is essential to understand the functional organization in the brain and to identify changes due to neurological disorders, development, treatment, and other phenomena.
Externí odkaz:
http://arxiv.org/abs/2311.03791
Moving average processes driven by exponential-tailed L\'evy noise are important extensions of their Gaussian counterparts in order to capture deviations from Gaussianity, more flexible dependence structures, and sample paths with jumps. Popular exam
Externí odkaz:
http://arxiv.org/abs/2307.15796
Model checking is essential to evaluate the adequacy of statistical models and the validity of inferences drawn from them. Particularly, hierarchical models such as latent Gaussian models (LGMs) pose unique challenges as it is difficult to check assu
Externí odkaz:
http://arxiv.org/abs/2307.12365
Statistical analysis of extremes can be used to predict the probability of future extreme events, such as large rainfalls or devastating windstorms. The quality of these forecasts can be measured through scoring rules. Locally scale invariant scoring
Externí odkaz:
http://arxiv.org/abs/2306.15088