Zobrazeno 1 - 10
of 151
pro vyhledávání: '"LIQUET, Benoît"'
We present a new optimization method for the group selection problem in linear regression. In this problem, predictors are assumed to have a natural group structure and the goal is to select a small set of groups that best fits the response. The inco
Externí odkaz:
http://arxiv.org/abs/2404.13339
The selection of best variables is a challenging problem in supervised and unsupervised learning, especially in high dimensional contexts where the number of variables is usually much larger than the number of observations. In this paper, we focus on
Externí odkaz:
http://arxiv.org/abs/2403.20007
Compositional data find broad application across diverse fields due to their efficacy in representing proportions or percentages of various components within a whole. Spatial dependencies often exist in compositional data, particularly when the data
Externí odkaz:
http://arxiv.org/abs/2403.13076
Autor:
Bhaskaran, Aishwarya, Ma, Ding, Liquet, Benoit, Hong, Angela, Lo, Serigne N, Heritier, Stephane, Ma, Jun
Accelerated failure time (AFT) models are frequently used for modelling survival data. This approach is attractive as it quantifies the direct relationship between the time until an event occurs and various covariates. It asserts that the failure tim
Externí odkaz:
http://arxiv.org/abs/2403.12332
Autor:
Black, David, Gill, Jaidev, Xie, Andrew, Liquet, Benoit, Di leva, Antonio, Stummer, Walter, Molina, Eric Suero
Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables visualization of differences between tissues that are not distinguishable to humans. This augmentation can maximize brain tumor resection, improving patient outcomes. H
Externí odkaz:
http://arxiv.org/abs/2402.03761
Autor:
Black, David, Liquet, Benoit, Kaneko, Sadahiro, Di leva, Antonio, Stummer, Walter, Molina, Eric Suero
Through spectral unmixing, hyperspectral imaging (HSI) in fluorescence-guided brain tumor surgery has enabled detection and classification of tumor regions invisible to the human eye. Prior unmixing work has focused on determining a minimal set of vi
Externí odkaz:
http://arxiv.org/abs/2401.17388
We consider the problem of best subset selection in linear regression, where the goal is to find for every model size $k$, that subset of $k$ features that best fit the response. This is particularly challenging when the total available number of fea
Externí odkaz:
http://arxiv.org/abs/2205.02617
Spatial process models for capturing nonstationary behavior in scientific data present several challenges with regard to statistical inference and uncertainty quantification. While nonstationary spatially-varying kernels are attractive for their flex
Externí odkaz:
http://arxiv.org/abs/2203.11873
Autor:
Kermorvant, Claire, Liquet, Benoit, Litt, Guy, Mengersen, Kerrie, Peterson, Erin, Hyndman, Rob, Jones Jr., Jeremy B., Leigh, Catherine
Real time monitoring using in situ sensors is becoming a common approach for measuring water quality within watersheds. High frequency measurements produce big data sets that present opportunities to conduct new analyses for improved understanding of
Externí odkaz:
http://arxiv.org/abs/2106.01719
Even though Nearest Neighbor Gaussian Processes (NNGP) alleviate considerably MCMC implementation of Bayesian space-time models, they do not solve the convergence problems caused by high model dimension. Frugal alternatives such as response or collap
Externí odkaz:
http://arxiv.org/abs/2010.00896