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Статті в журналах з теми "Imputation de Valeurs manquantes":
Trempe, Normand, Marie-Claude Boivin, Ernest Lo, and Amadou Diogo Barry. "L’utilisation de la variable sur la langue d’usage à la maison du Registre des décès du Québec." Notes de recherche 43, no. 1 (June 4, 2014): 163–80. http://dx.doi.org/10.7202/1025494ar.
Basham, C. Andrew. "Variations régionales de prévalence de la multimorbidité en Colombie-Britannique (Canada) : analyse transversale des données de l’Enquête sur la santé dans les collectivités canadiennes de 2015-2016." Promotion de la santé et prévention des maladies chroniques au Canada 40, no. 7/8 (July 2020): 251–61. http://dx.doi.org/10.24095/hpcdp.40.7/8.02f.
Montreuil, Sylvie, Richard Halley, and Shirley Joe. "Poids à la naissance et durée de gestation manquants? La solution dans le jumelage des fichiers des naissances et des hospitalisations." Notes de recherche 25, no. 2 (March 25, 2004): 261–78. http://dx.doi.org/10.7202/010212ar.
Ben Othman, Leila, François Rioult, Sadok Ben Yahia, and Bruno Crémilleux. "Base de caractérisation des valeurs manquantes." Techniques et sciences informatiques 30, no. 10 (December 28, 2011): 1247–70. http://dx.doi.org/10.3166/tsi.30.1247-1270.
Galimard, J. E., S. Chevret, and M. Resche-Rigon. "Imputation multiple en présence de données manquantes MNAR." Revue d'Épidémiologie et de Santé Publique 63 (May 2015): S42. http://dx.doi.org/10.1016/j.respe.2015.03.014.
Rossel, F., and J. Garbrecht. "Analyse et amélioration d'un indice pluviométrique mensuel régional pour les grandes plaines du sud des États-Unis." Revue des sciences de l'eau 13, no. 1 (April 12, 2005): 39–46. http://dx.doi.org/10.7202/705379ar.
Aurélien, Njamen Kengdo Arsène, and Kwatcho Kengdo Steve. "Gestion Des Donnees Manquantes Dans Les Bases De Donnees En Sciences Sociales : Algorithme Nipals Ou Imputation Multiple?" European Scientific Journal, ESJ 12, no. 35 (December 31, 2016): 390. http://dx.doi.org/10.19044/esj.2016.v12n35p390.
Doggett, Amanda, Ashok Chaurasia, Jean-Philippe Chaput, and Scott T. Leatherdale. "Utilisation des arbres de classification et de régression pour modéliser les données manquantes sur l’IMC, la taille et la masse corporelle chez les jeunes." Promotion de la santé et prévention des maladies chroniques au Canada 43, no. 5 (May 2023): 257–69. http://dx.doi.org/10.24095/hpcdp.43.5.03f.
Badisy, I. El, C. Nejjari, A. Naim, K. El Rhaz, M. Khalis, and R. Giorgi. "CO10.6 - Imputation des données manquantes par un méta-algorithme (metaCART): étude de simulation." Revue d'Épidémiologie et de Santé Publique 71 (May 2023): 101632. http://dx.doi.org/10.1016/j.respe.2023.101632.
Soullier, N., E. de la Rochebrochard, and J. Bouyer. "Imputation multiple et répartition des données manquantes dans les cohortes : exemple de la fécondation in vitro." Revue d'Épidémiologie et de Santé Publique 56, no. 5 (September 2008): 276. http://dx.doi.org/10.1016/j.respe.2008.06.077.
Дисертації з теми "Imputation de Valeurs manquantes":
Bernard, Francis. "Méthodes d'analyse des données incomplètes incorporant l'incertitude attribuable aux valeurs manquantes." Mémoire, Université de Sherbrooke, 2013. http://hdl.handle.net/11143/6571.
Etourneau, Lucas. "Contrôle du FDR et imputation de valeurs manquantes pour l'analyse de données de protéomiques par spectrométrie de masse." Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALS001.
Proteomics involves characterizing the proteome of a biological sample, that is, the set of proteins it contains, and doing so as exhaustively as possible. By identifying and quantifying protein fragments that are analyzable by mass spectrometry (known as peptides), proteomics provides access to the level of gene expression at a given moment. This is crucial information for improving the understanding of molecular mechanisms at play within living organisms. These experiments produce large amounts of data, often complex to interpret and subject to various biases. They require reliable data processing methods that ensure a certain level of quality control, as to guarantee the relevance of the resulting biological conclusions.The work of this thesis focuses on improving this data processing, and specifically on the following two major points:The first is controlling for the false discovery rate (FDR), when either identifying (1) peptides or (2) quantitatively differential biomarkers between a tested biological condition and its negative control. Our contributions focus on establishing links between the empirical methods stemmed for proteomic practice and other theoretically supported methods. This notably allows us to provide directions for the improvement of FDR control methods used for peptide identification.The second point focuses on managing missing values, which are often numerous and complex in nature, making them impossible to ignore. Specifically, we have developed a new algorithm for imputing them that leverages the specificities of proteomics data. Our algorithm has been tested and compared to other methods on multiple datasets and according to various metrics, and it generally achieves the best performance. Moreover, it is the first algorithm that allows imputation following the trending paradigm of "multi-omics": if it is relevant to the experiment, it can impute more reliably by relying on transcriptomic information, which quantifies the level of messenger RNA expression present in the sample. Finally, Pirat is implemented in a freely available software package, making it easy to use for the proteomic community
Morisot, Adeline. "Méthodes d’analyse de survie, valeurs manquantes et fractions attribuables temps dépendantes : application aux décès par cancer de la prostate." Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTT010/document.
The term survival analysis refers to methods used for modeling the time of occurrence of one or more events taking censoring into account. The event of interest may be either the onset or the recurrence of a disease, or death. The causes of death may have missing values, a status that may be modeled by imputation methods. In the first section of this thesis we made a review of the methods used to deal with these missing data. Then, we detailed the procedures that enable multiple imputation of causes of death. We have developed these methods in a subset of the ERSPC (European Randomized Study of Screening for Prostate Cancer), which studied screening and mortality for prostate cancer. We proposed a theoretical formulation of Rubin rules after a complementary log-log transformation to combine estimates of survival. In addition, we provided the related R code. In a second section, we presented the survival analysis methods, by proposing a unified writing based on the definitions of crude and net survival, while considering either all-cause or specific cause of death. This involves consideration of censoring which can then be informative. We considered the so-called traditional methods (Kaplan-Meier, Nelson-Aalen, Cox and parametric) methods of competing risks (considering a multistate model or a latent failure time model), methods called specific that are corrected using IPCW (Inverse Ponderation Censoring Weighting) and relative survival methods. The classical methods are based on a non-informative censoring assumption. When we are interested in deaths from all causes, this assumption is often valid. However, for a particular cause of death, other causes of death are considered as a censoring. In this case, censoring by other causes of death is generally considered informative. We introduced an approach based on the IPCW method to correct this informative censoring, and we provided an R function to apply this approach directly. All methods presented in this chapter were applied to datasets completed by multiple imputation. Finally, in a last part we sought to determine the percentage of deaths explained by one or more variables using attributable fractions. We presented the theoretical formulations of attributable fractions, time-independent and time-dependent that are expressed as survival. We illustrated these concepts using all the survival methods presented in section 2, and compared the results. Estimates obtained with the different methods were very similar
Chion, Marie. "Développement de nouvelles méthodologies statistiques pour l'analyse de données de protéomique quantitative." Thesis, Strasbourg, 2021. http://www.theses.fr/2021STRAD025.
Proteomic analysis consists of studying all the proteins expressed by a given biological system, at a given time and under given conditions. Recent technological advances in mass spectrometry and liquid chromatography make it possible to envisage large-scale and high-throughput proteomic studies.This thesis work focuses on developing statistical methodologies for the analysis of quantitative proteomics data and thus presents three main contributions. The first part proposes to use monotone spline regression models to estimate the amounts of all peptides detected in a sample using internal standards labelled for a subset of targeted peptides. The second part presents a strategy to account for the uncertainty induced by the multiple imputation process in the differential analysis, also implemented in the mi4p R package. Finally, the third part proposes a Bayesian framework for differential analysis, making it notably possible to consider the correlations between the intensities of peptides
Moreno, Betancur Margarita. "Regression modeling with missing outcomes : competing risks and longitudinal data." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA11T076/document.
Missing data are a common occurrence in medical studies. In regression modeling, missing outcomes limit our capability to draw inferences about the covariate effects of medical interest, which are those describing the distribution of the entire set of planned outcomes. In addition to losing precision, the validity of any method used to draw inferences from the observed data will require that some assumption about the mechanism leading to missing outcomes holds. Rubin (1976, Biometrika, 63:581-592) called the missingness mechanism MAR (for “missing at random”) if the probability of an outcome being missing does not depend on missing outcomes when conditioning on the observed data, and MNAR (for “missing not at random”) otherwise. This distinction has important implications regarding the modeling requirements to draw valid inferences from the available data, but generally it is not possible to assess from these data whether the missingness mechanism is MAR or MNAR. Hence, sensitivity analyses should be routinely performed to assess the robustness of inferences to assumptions about the missingness mechanism. In the field of incomplete multivariate data, in which the outcomes are gathered in a vector for which some components may be missing, MAR methods are widely available and increasingly used, and several MNAR modeling strategies have also been proposed. On the other hand, although some sensitivity analysis methodology has been developed, this is still an active area of research. The first aim of this dissertation was to develop a sensitivity analysis approach for continuous longitudinal data with drop-outs, that is, continuous outcomes that are ordered in time and completely observed for each individual up to a certain time-point, at which the individual drops-out so that all the subsequent outcomes are missing. The proposed approach consists in assessing the inferences obtained across a family of MNAR pattern-mixture models indexed by a so-called sensitivity parameter that quantifies the departure from MAR. The approach was prompted by a randomized clinical trial investigating the benefits of a treatment for sleep-maintenance insomnia, from which 22% of the individuals had dropped-out before the study end. The second aim was to build on the existing theory for incomplete multivariate data to develop methods for competing risks data with missing causes of failure. The competing risks model is an extension of the standard survival analysis model in which failures from different causes are distinguished. Strategies for modeling competing risks functionals, such as the cause-specific hazards (CSH) and the cumulative incidence function (CIF), generally assume that the cause of failure is known for all patients, but this is not always the case. Some methods for regression with missing causes under the MAR assumption have already been proposed, especially for semi-parametric modeling of the CSH. But other useful models have received little attention, and MNAR modeling and sensitivity analysis approaches have never been considered in this setting. We propose a general framework for semi-parametric regression modeling of the CIF under MAR using inverse probability weighting and multiple imputation ideas. Also under MAR, we propose a direct likelihood approach for parametric regression modeling of the CSH and the CIF. Furthermore, we consider MNAR pattern-mixture models in the context of sensitivity analyses. In the competing risks literature, a starting point for methodological developments for handling missing causes was a stage II breast cancer randomized clinical trial in which 23% of the deceased women had missing cause of death. We use these data to illustrate the practical value of the proposed approaches
Fiot, Céline. "Extraction de séquences fréquentes : des données numériques aux valeurs manquantes." Phd thesis, Montpellier 2, 2007. http://www.theses.fr/2007MON20056.
Fiot, Céline. "Extraction de séquences fréquentes : des données numériques aux valeurs manquantes." Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2007. http://tel.archives-ouvertes.fr/tel-00179506.
Audigier, Vincent. "Imputation multiple par analyse factorielle : Une nouvelle méthodologie pour traiter les données manquantes." Thesis, Rennes, Agrocampus Ouest, 2015. http://www.theses.fr/2015NSARG015/document.
This thesis proposes new multiple imputation methods that are based on principal component methods, which were initially used for exploratory analysis and visualisation of continuous, categorical and mixed multidimensional data. The study of principal component methods for imputation, never previously attempted, offers the possibility to deal with many types and sizes of data. This is because the number of estimated parameters is limited due to dimensionality reduction.First, we describe a single imputation method based on factor analysis of mixed data. We study its properties and focus on its ability to handle complex relationships between variables, as well as infrequent categories. Its high prediction quality is highlighted with respect to the state-of-the-art single imputation method based on random forests.Next, a multiple imputation method for continuous data using principal component analysis (PCA) is presented. This is based on a Bayesian treatment of the PCA model. Unlike standard methods based on Gaussian models, it can still be used when the number of variables is larger than the number of individuals and when correlations between variables are strong.Finally, a multiple imputation method for categorical data using multiple correspondence analysis (MCA) is proposed. The variability of prediction of missing values is introduced via a non-parametric bootstrap approach. This helps to tackle the combinatorial issues which arise from the large number of categories and variables. We show that multiple imputation using MCA outperforms the best current methods
RAGEL, ARNAUD. "Exploration des bases incompletes application a l'aide au pretraitement des valeurs manquantes." Caen, 1999. http://www.theses.fr/1999CAEN2067.
Ben, Othman Leila. "Conception et validation d'une méthode de complétion des valeurs manquantes fondée sur leurs modèles d'apparition." Phd thesis, Université de Caen, 2011. http://tel.archives-ouvertes.fr/tel-01017941.
Книги з теми "Imputation de Valeurs manquantes":
Buuren, Stef van. Flexible imputation of missing data. Boca Raton: CRC Press, 2012.
Raghunathan, Trivellore, Patricia A. Berglund, and Peter W. Solenberger. Multiple Imputation in Practice: With Examples Using IVEware. Taylor & Francis Group, 2018.
Raghunathan, Trivellore, Patricia A. Berglund, and Peter W. Solenberger. Multiple Imputation in Practice: With Examples Using IVEware. Taylor & Francis Group, 2018.
Raghunathan, Trivellore, Patricia A. Berglund, and Peter W. Solenberger. Multiple Imputation in Practice: With Examples Using IVEware. Taylor & Francis Group, 2018.
Buuren, Stef van. Flexible Imputation of Missing Data, Second Edition. Taylor & Francis Group, 2018.
Buuren, Stef van. Flexible Imputation of Missing Data, Second Edition. Taylor & Francis Group, 2018.
Buuren, Stef van. Flexible Imputation of Missing Data Second Edition. Taylor & Francis Group, 2021.
Buuren, Stef van. Flexible Imputation of Missing Data, Second Edition. Taylor & Francis Group, 2018.
Buuren, Stef van. Flexible Imputation of Missing Data, Second Edition. Taylor & Francis Group, 2018.
Buuren, Stef van. Flexible Imputation of Missing Data. Taylor & Francis Group, 2012.