Results 201 to 210 of about 114,046 (322)
The Gauss map of Minimal graphs in the Heisenberg group [PDF]
Christiam Figueroa
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ABSTRACT There is an increased proportion of studies using quantile‐based regression methodology (QR) in economics. They offer a robust alternative to classical mean regressions, which can estimate non‐normal variables with distributional heterogeneity in the dependent variable.
Shajara Ul‐Durar +4 more
wiley +1 more source
Targetless LiDAR-camera extrinsic calibration via semantic distribution alignment. [PDF]
Chen X, Sun B.
europepmc +1 more source
ON THE GAUSS MAP OF TRANSLATION SURFACES IN MINKOWSKI 3-SPACE [PDF]
Dae Won Yoon
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The Tension Field of the Gauss Map [PDF]
Ruh, E. A., Vilms, J.
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Nonparametric Detection of a Time‐Varying Mean
ABSTRACT We propose a nonparametric portmanteau test for detecting changes in the unconditional mean of a univariate time series which may display either long or short memory. Our approach is designed to have power against, among other things, cases where the mean component of the series displays abrupt level shifts, deterministic trending behaviour ...
Fabrizio Iacone, A. M. Robert Taylor
wiley +1 more source
DPCR-SLAM: A Dual-Point-Cloud-Registration SLAM Based on Line Features for Mapping an Indoor Mobile Robot. [PDF]
Cao Y, Ni J, Huang Y.
europepmc +1 more source
On the volume and the Gauss map image of spacelike hypersurfaces in de Sitter space [PDF]
Juan A. Aledo, Luis J. Alı́as
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Robust Bernoulli Mixture Models for Credit Portfolio Risk
ABSTRACT This paper presents comparison results and establishes risk bounds for credit portfolios within classes of Bernoulli mixture models, assuming conditionally independent defaults that are stochastically increasing in a common risk factor. We provide simple and interpretable conditions on conditional default probabilities that imply a comparison ...
Jonathan Ansari, Eva Lütkebohmert
wiley +1 more source
Reinforcement Learning for Jump‐Diffusions, With Financial Applications
ABSTRACT We study continuous‐time reinforcement learning (RL) for stochastic control in which system dynamics are governed by jump‐diffusion processes. We formulate an entropy‐regularized exploratory control problem with stochastic policies to capture the exploration–exploitation balance essential for RL.
Xuefeng Gao, Lingfei Li, Xun Yu Zhou
wiley +1 more source

