Search Results - "Josse, Julie"

missMDA : A Package for Handling Missing Values in Multivariate Data Analysis by Josse, Julie, Husson, François

“…We present the R package missMDA which performs principal component methods on incomplete data sets, aiming to obtain scores, loadings and graphical…”
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FactoMineR : An R Package for Multivariate Analysis by Lê, Sébastien, Josse, Julie, Husson, François

“…In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into…”
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Selecting the number of components in principal component analysis using cross-validation approximations by Josse, Julie, Husson, François

“…Cross-validation is a tried and tested approach to select the number of components in principal component analysis (PCA), however, its main drawback is its…”
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Causal effect on a target population: A sensitivity analysis to handle missing covariates by Colnet, Bénédicte, Josse, Julie, Varoquaux, Gaël, Scornet, Erwan

“…Randomized controlled trials (RCTs) are often considered the gold standard for estimating causal effect, but they may lack external validity when the…”
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Association between in-ICU red blood cells transfusion and 1-year mortality in ICU survivors by Blet, Alice, McNeil, Joel B, Josse, Julie, Cholley, Bernard, Cinotti, Raphaël, Cotter, Gad, Dauvergne, Agnès, Davison, Beth, Duarte, Kévin, Duranteau, Jacques, Fournier, Marie-Céline, Gayat, Etienne, Jaber, Samir, Lasocki, Sigismond, Merkling, Thomas, Peoc'h, Katell, Mayer, Imke, Sadoune, Malha, Laterre, Pierre-François, Sonneville, Romain, Ware, Lorraine, Mebazaa, Alexandre, Kimmoun, Antoine

“…Abstract Background Impact of in-ICU transfusion on long-term outcomes remains unknown. The purpose of this study was to assess in critical-care survivors the…”
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Jan de Leeuw and the French School of Data Analysis by Husson, François, Josse, Julie, Saporta, Gilbert

“…The Dutch and the French schools of data analysis differ in their approaches to the question: How does one understand and summarize the information contained…”
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Pilot deployment of a machine-learning enhanced prediction of need for hemorrhage resuscitation after trauma - the ShockMatrix pilot study by Gauss, Tobias, Moyer, Jean-Denis, Colas, Clelia, Pichon, Manuel, Delhaye, Nathalie, Werner, Marie, Ramonda, Veronique, Sempe, Theophile, Medjkoune, Sofiane, Josse, Julie, James, Arthur, Harrois, Anatole

“…Decision-making in trauma patients remains challenging and often results in deviation from guidelines. Machine-Learning (ML) enhanced decision-support could…”
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Generalizing treatment effects with incomplete covariates: Identifying assumptions and multiple imputation algorithms by Mayer, Imke, Josse, Julie

“…We focus on the problem of generalizing a causal effect estimated on a randomized controlled trial (RCT) to a target population described by a set of…”
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Principal component analysis with missing values: a comparative survey of methods by Dray, Stéphane, Josse, Julie

“…Principal component analysis (PCA) is a standard technique to summarize the main structures of a data table containing the measurements of several quantitative…”
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Adaptive shrinkage of singular values by Josse, Julie, Sardy, Sylvain

“…To recover a low-rank structure from a noisy matrix, truncated singular value decomposition has been extensively used and studied. Recent studies suggested…”
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Logistic regression with missing covariates—Parameter estimation, model selection and prediction within a joint-modeling framework by Jiang, Wei, Josse, Julie, Lavielle, Marc

“…Logistic regression is a common classification method in supervised learning. Surprisingly, there are very few solutions for performing logistic regression…”
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Multiple correspondence analysis and the multilogit bilinear model by Fithian, William, Josse, Julie

“…Multiple correspondence analysis is a dimension reduction technique which plays a large role in the analysis of tables with categorical nominal variables, such…”
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Imputation and low-rank estimation with Missing Not At Random data by Sportisse, Aude, Boyer, Claire, Josse, Julie

“…Missing values challenge data analysis because many supervised and unsupervised learning methods cannot be applied directly to incomplete data. Matrix…”
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50 years of data sciences, discussion by Holmes, Susan, Josse, Julie

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Discussion of "50 Years of Data Science" by Holmes, Susan, Josse, Julie

“…First of all, we would like to thank the author for writing such a thoughtful article. The article draws attention to so many important aspects at the…”
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Nonparametric Imputation by Data Depth by Mozharovskyi, Pavlo, Josse, Julie, Husson, François

“…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…”
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Main Effects and Interactions in Mixed and Incomplete Data Frames by Robin, Geneviève, Klopp, Olga, Josse, Julie, Moulines, Éric, Tibshirani, Robert

“…A mixed data frame (MDF) is a table collecting categorical, numerical, and count observations. The use of MDF is widespread in statistics and the applications…”
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A principal component method to impute missing values for mixed data by Audigier, Vincent, Husson, François, Josse, Julie

“…We propose a new method to impute missing values in mixed data sets. It is based on a principal component method, the factorial analysis for mixed data, which…”
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Logistic Regression with Missing Covariates -- Parameter Estimation, Model Selection and Prediction by Jiang, Wei, Josse, Julie, Lavielle, Marc

“…Logistic regression is a common classification method in supervised learning. Surprisingly , there are very few solutions for performing it and selecting…”
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Robust Lasso‐Zero for sparse corruption and model selection with missing covariates by Descloux, Pascaline, Boyer, Claire, Josse, Julie, Sportisse, Aude, Sardy, Sylvain

“…We propose Robust Lasso‐Zero, an extension of the Lasso‐Zero methodology, initially introduced for sparse linear models, to the sparse corruptions problem. We…”
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