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pro vyhledávání: '"62h11"'
Fern\'andez-Dur\'an (2004) developed a family of circular distributions based on nonnegative trigonometric sums (NNTS) which is flexible for modeling datasets exhibiting multimodality and asymmetry. Many datasets involving angles in the natural scien
Externí odkaz:
http://arxiv.org/abs/2412.19501
Autor:
Gierse, Jana, Fried, Roland
This paper proposes robust estimators of the variogram, a statistical tool that is commonly used in geostatistics to capture the spatial dependence structure of data. The new estimators are based on the highly robust minimum covariance determinant es
Externí odkaz:
http://arxiv.org/abs/2412.01464
Autor:
Chacón, José E., Meilán-Vila, Andrea
This paper presents an alternative formulation of the geodesic normal distribution on the sphere, building on the work of Hauberg (2018). While the isotropic version of this distribution is naturally defined on the sphere, the anisotropic version req
Externí odkaz:
http://arxiv.org/abs/2411.14899
A kernel density estimator for data on the polysphere $\mathbb{S}^{d_1}\times\cdots\times\mathbb{S}^{d_r}$, with $r,d_1,\ldots,d_r\geq 1$, is presented in this paper. We derive the main asymptotic properties of the estimator, including mean square er
Externí odkaz:
http://arxiv.org/abs/2411.04166
We consider the projected normal distribution, with isotropic variance, on the 2-sphere using intrinsic statistics. We show that in this case, the expectation commutes with the projection and that the covariance of the normal variable has a 1-1 corre
Externí odkaz:
http://arxiv.org/abs/2410.22384
We introduce the extremal range, a local statistic for studying the spatial extent of extreme events in random fields on $\mathbb{R}^d$. Conditioned on exceedance of a high threshold at a location $s$, the extremal range at $s$ is the random variable
Externí odkaz:
http://arxiv.org/abs/2411.02399
Autor:
Szabo, Botond, Zhu, Yichen
Gaussian Processes (GPs) are widely used to model dependency in spatial statistics and machine learning, yet the exact computation suffers an intractable time complexity of $O(n^3)$. Vecchia approximation allows scalable Bayesian inference of GPs in
Externí odkaz:
http://arxiv.org/abs/2410.10649
It is no secret that statistical modelling often involves making simplifying assumptions when attempting to study complex stochastic phenomena. Spatial modelling of extreme values is no exception, with one of the most common such assumptions being st
Externí odkaz:
http://arxiv.org/abs/2409.16373
Autor:
Tiepner, Anton, Trottner, Lukas
We study a stochastic heat equation with piecewise constant diffusivity $\theta$ having a jump at a hypersurface $\Gamma$ that splits the underlying space $[0,1]^d$, $d\geq2,$ into two disjoint sets $\Lambda_-\cup\Lambda_+.$ Based on multiple spatial
Externí odkaz:
http://arxiv.org/abs/2409.15059
Autor:
Gräfensteiner, Phillip, Osenberg, Markus, Hilger, André, Bohn, Nicole, Binder, Joachim R., Manke, Ingo, Schmidt, Volker, Neumann, Matthias
A stochastic 3D modeling approach for the nanoporous binder-conductive additive phase in hierarchically structured cathodes of lithium-ion batteries is presented. The binder-conductive additive phase of these electrodes consists of carbon black, poly
Externí odkaz:
http://arxiv.org/abs/2409.11080