Zobrazeno 1 - 10
of 184
pro vyhledávání: '"Husson François"'
Autor:
Laas, Enora, Dumas, Elise, Hamy, Anne-Sophie, Gaillard, Thomas, Gougis, Paul, Reyal, Fabien, Husson, François, Jannot, Anne-Sophie
Publikováno v:
In European Journal of Surgical Oncology January 2025 51(1)
Publikováno v:
BMC Genomics, Vol 10, Iss 1, p 32 (2009)
Abstract Background Genomic analysis will greatly benefit from considering in a global way various sources of molecular data with the related biological knowledge. It is thus of great importance to provide useful integrative approaches dedicated to e
Externí odkaz:
https://doaj.org/article/851a01890e4a47e192355995960a6674
Statistical analysis of large data sets offers new opportunities to better understand many processes. Yet, data accumulation often implies relaxing acquisition procedures or compounding diverse sources. As a consequence, such data sets often contain
Externí odkaz:
http://arxiv.org/abs/1804.11087
Autor:
Gharbi-Meliani, Amin1 (AUTHOR), Husson, François2 (AUTHOR), Vandendriessche, Henri3 (AUTHOR), Bayen, Eleonore4,5 (AUTHOR), Yaffe, Kristine5,6 (AUTHOR), Bachoud-Lévi, Anne-Catherine1,7 (AUTHOR), Cleret de Langavant, Laurent1,5,7 (AUTHOR) laurent.cleret@gbhi.org
Publikováno v:
Alzheimer's Research & Therapy. 11/29/2023, Vol. 15 Issue 1, p1-11. 11p.
We present single imputation method for missing values which borrows the idea of data depth---a measure of centrality defined for an arbitrary point of a space with respect to a probability distribution or data cloud. This consists in iterative maxim
Externí odkaz:
http://arxiv.org/abs/1701.03513
We propose a multiple imputation method to deal with incomplete categorical data. This method imputes the missing entries using the principal components method dedicated to categorical data: multiple correspondence analysis (MCA). The uncertainty con
Externí odkaz:
http://arxiv.org/abs/1505.08116
PCA is often used to visualize data when the rows and the columns are both of interest. In such a setting there is a lack of inferential methods on the PCA output. We study the asymptotic variance of a fixed-effects model for PCA, and propose several
Externí odkaz:
http://arxiv.org/abs/1407.7614
We propose a multiple imputation method based on principal component analysis (PCA) to deal with incomplete continuous data. To reflect the uncertainty of the parameters from one imputation to the next, we use a Bayesian treatment of the PCA model. U
Externí odkaz:
http://arxiv.org/abs/1401.5747
We propose a new method to impute missing values in mixed datasets. It is based on a principal components method, the factorial analysis for mixed data, which balances the influence of all the variables that are continuous and categorical in the cons
Externí odkaz:
http://arxiv.org/abs/1301.4797
Principal component analysis (PCA) is a well-established method commonly used to explore and visualise data. A classical PCA model is the fixed effect model where data are generated as a fixed structure of low rank corrupted by noise. Under this mode
Externí odkaz:
http://arxiv.org/abs/1301.4649