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ICAPR
2005
Springer
14 years 27 days ago
Missing Data Estimation Using Polynomial Kernels
Abstract. In this paper, we deal with the problem of partially observed objects. These objects are defined by a set of points and their shape variations are represented by a statis...
Maxime Berar, Michel Desvignes, Gérard Bail...
ENVSOFT
2008
175views more  ENVSOFT 2008»
13 years 7 months ago
Automated regression-based statistical downscaling tool
Many impact studies require climate change information at a finer resolution than that provided by Global Climate Models (GCMs). In the last 10 years, downscaling techniques, both...
Masoud Hessami, Philippe Gachon, Taha B. M. J. Oua...
JMLR
2010
145views more  JMLR 2010»
13 years 2 months ago
Kernel Partial Least Squares is Universally Consistent
We prove the statistical consistency of kernel Partial Least Squares Regression applied to a bounded regression learning problem on a reproducing kernel Hilbert space. Partial Lea...
Gilles Blanchard, Nicole Krämer
DAC
2006
ACM
14 years 8 months ago
Statistical timing based on incomplete probabilistic descriptions of parameter uncertainty
Existing approaches to timing analysis under uncertainty are based on restrictive assumptions. Statistical STA techniques assume that the full probabilistic distribution of parame...
Wei-Shen Wang, Vladik Kreinovich, Michael Orshansk...
SIAMSC
2011
219views more  SIAMSC 2011»
13 years 2 months ago
Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian
We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inf...
H. P. Flath, Lucas C. Wilcox, Volkan Akcelik, Judi...