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Добірка наукової літератури з теми "Inférence sélective"
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Статті в журналах з теми "Inférence sélective"
Nicolet, Gilles, Nicolas Eckert, Samuel Morin, and Juliette Blanchet. "Inférence et modélisation de la dépendance spatiale des extrêmes neigeux dans les Alpes françaises par processus max-stables." La Houille Blanche, no. 5-6 (December 2019): 150–58. http://dx.doi.org/10.1051/lhb/2019047.
Повний текст джерелаDupuy, Claire, Ferdinand Teuber, and Virginie Van Ingelgom. "Citizens’ experiences of a policy-ridden environment: A methodological contribution to feedback studies based on qualitative secondary analysis." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 156, no. 1 (October 2022): 124–57. http://dx.doi.org/10.1177/07591063221132342.
Повний текст джерелаДисертації з теми "Inférence sélective"
Yadegari, Iraj. "Prédiction, inférence sélective et quelques problèmes connexes." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10167.
Повний текст джерелаAbstract : We study the problem of point estimation and predictive density estimation of the mean of a selected population, obtaining novel developments which include bias analysis, decomposition of risk, and problems with restricted parameters (Chapter 2). We propose efficient predictive density estimators in terms of Kullback-Leibler and Hellinger losses (Chapter 3) improving on plug-in procedures via a dual loss and via a variance expansion scheme. Finally (Chapter 4), we present findings on improving on the maximum likelihood estimator (MLE) of a bounded normal mean under a class of loss functions, including reflected normal loss, with implications for predictive density estimation. Namely, we give conditions on the loss and the width of the parameter space for which the Bayes estimator with respect to the boundary uniform prior dominates the MLE.
Hivert, Benjamin. "Clustering et analyse différentielle de données d'expression génique." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0171.
Повний текст джерелаAnalyses of gene expression data obtained from bulk RNA sequencing (bulk RNA-seq) or single-cell RNA sequencing (scRNA-seq) have become commonplace in immunological studies. They allow for a better understanding of the heterogeneity present in immune responses, whether in reaction to vaccination or disease. Typically, the analysis of these data is conducted in two steps : i) first, an unsupervised classification, or clustering, is performed using all the genes to group samples into distinct and homogeneous subgroups ; ii) then, differential analysis is conducted using hypothesis tests to identify genes that are differentially expressed between these subgroups. However, these two successive steps lead to methodological challenge that is often overlooked in the applied literature. Traditional inference methods require hypothesis to be fixed a priori and independent of the data to ensure effective control of type I error. In the context of these two-steps analyses, the hypothesis tests are based on the results of the clustering, which compromises the control of type I error by traditional methods and can lead to false discoveries. We propose new statistical methods that account for this double use of the data and ensure an effective control of the number of false discoveries
Durand, Jean-Baptiste. "Modèles à structure cachée : inférence, estimation, sélection de modèles et applications." Phd thesis, Université Joseph Fourier (Grenoble), 2003. https://tel.archives-ouvertes.fr/tel-00002754v3.
Повний текст джерелаCaron, François. "Inférence bayésienne pour la détermination et la sélection de modèles stochastiques." Ecole Centrale de Lille, 2006. http://www.theses.fr/2006ECLI0012.
Повний текст джерелаWe are interested in the addition of uncertainty in hidden Markov models. The inference is made in a Bayesian framework based on Monte Carlo methods. We consider multiple sensors that may switch between several states of work. An original jump model is developed for different kind of situations, including synchronous/asynchronous data and the binary valid/invalid case. The model/algorithm is applied to the positioning of a land vehicle equipped with three sensors. One of them is a GPS receiver, whose data are potentially corrupted due to multipaths phenomena. We consider the estimation of the probability density function of the evolution and observation noises in hidden Markov models. First, the case of linear models is addressed and MCMC and particle filter algorithms are developed and applied on three different applications. Then the case of the estimation of probability density functions in nonlinear models is addressed. For that purpose, time-varying Dirichlet processes are defined for the online estimation of time-varying probability density functions
Guilloux, Agathe. "Inférence non paramétrique en statistique des durées de vie sous biais de sélection." Rennes 1, 2004. http://www.theses.fr/2004REN10058.
Повний текст джерелаDelattre, Maud. "Inférence statistique dans les modèles mixtes à dynamique Markovienne." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00765708.
Повний текст джерелаKarmann, Clémence. "Inférence de réseaux pour modèles inflatés en zéro." Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0146/document.
Повний текст джерелаNetwork inference has more and more applications, particularly in human health and environment, for the study of micro-biological and genomic data. Networks are indeed an appropriate tool to represent, or even study, relationships between entities. Many mathematical estimation techniques have been developed, particularly in the context of Gaussian graphical models, but also in the case of binary or mixed data. The processing of abundance data (of microorganisms such as bacteria for example) is particular for two reasons: on the one hand they do not directly reflect reality because a sequencing process takes place to duplicate species and this process brings variability, on the other hand a species may be absent in some samples. We are then in the context of zero-inflated data. Many graph inference methods exist for Gaussian, binary and mixed data, but zero-inflated models are rarely studied, although they reflect the structure of many data sets in a relevant way. The objective of this thesis is to infer networks for zero-inflated models. In this thesis, we will restrict to conditional dependency graphs. The work presented in this thesis is divided into two main parts. The first one concerns graph inference methods based on the estimation of neighbourhoods by a procedure combining ordinal regression models and variable selection methods. The second one focuses on graph inference in a model where the variables are Gaussian zero-inflated by double truncation (right and left)
Gallopin, Mélina. "Classification et inférence de réseaux pour les données RNA-seq." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLS174/document.
Повний текст джерелаThis thesis gathers methodologicals contributions to the statistical analysis of next-generation high-throughput transcriptome sequencing data (RNA-seq). RNA-seq data are discrete and the number of samples sequenced is usually small due to the cost of the technology. These two points are the main statistical challenges for modelling RNA-seq data.The first part of the thesis is dedicated to the co-expression analysis of RNA-seq data using model-based clustering. A natural model for discrete RNA-seq data is a Poisson mixture model. However, a Gaussian mixture model in conjunction with a simple transformation applied to the data is a reasonable alternative. We propose to compare the two alternatives using a data-driven criterion to select the model that best fits each dataset. In addition, we present a model selection criterion to take into account external gene annotations. This model selection criterion is not specific to RNA-seq data. It is useful in any co-expression analysis using model-based clustering designed to enrich functional annotation databases.The second part of the thesis is dedicated to network inference using graphical models. The aim of network inference is to detect relationships among genes based on their expression. We propose a network inference model based on a Poisson distribution taking into account the discrete nature and high inter sample variability of RNA-seq data. However, network inference methods require a large number of samples. For Gaussian graphical models, we propose a non-asymptotic approach to detect relevant subsets of genes based on a block-diagonale decomposition of the covariance matrix. This method is not specific to RNA-seq data and reduces the dimension of any network inference problem based on the Gaussian graphical model
Karmann, Clémence. "Inférence de réseaux pour modèles inflatés en zéro." Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0146.
Повний текст джерелаNetwork inference has more and more applications, particularly in human health and environment, for the study of micro-biological and genomic data. Networks are indeed an appropriate tool to represent, or even study, relationships between entities. Many mathematical estimation techniques have been developed, particularly in the context of Gaussian graphical models, but also in the case of binary or mixed data. The processing of abundance data (of microorganisms such as bacteria for example) is particular for two reasons: on the one hand they do not directly reflect reality because a sequencing process takes place to duplicate species and this process brings variability, on the other hand a species may be absent in some samples. We are then in the context of zero-inflated data. Many graph inference methods exist for Gaussian, binary and mixed data, but zero-inflated models are rarely studied, although they reflect the structure of many data sets in a relevant way. The objective of this thesis is to infer networks for zero-inflated models. In this thesis, we will restrict to conditional dependency graphs. The work presented in this thesis is divided into two main parts. The first one concerns graph inference methods based on the estimation of neighbourhoods by a procedure combining ordinal regression models and variable selection methods. The second one focuses on graph inference in a model where the variables are Gaussian zero-inflated by double truncation (right and left)
Maurent, Eliott. "Des forêts tropicales et des humains dans les Amériques : trajectoires de réponse aux perturbations anthropiques de la diversité et de la composition des arbres. Of tropical forests and humans in the Americas : response trajectories of tree diversity and composition to anthropogenic disturbances." Electronic Thesis or Diss., Paris, AgroParisTech, 2023. http://www.theses.fr/2023AGPT0014.
Повний текст джерелаTropical forests face more frequent and intense anthropogenic disturbances, such as selective logging, namely the felling and harvesting of a few commercially valuable trees in old-growth forests, while the remaining stand is left for natural regeneration. Many studies focused on this regeneration, particularly on the recovery of carbon and timber stocks, most likely due to a strong interest in climate change mitigation and logging profitability. However, despite the crucial role of biodiversity for ecosystem maintenance and functioning - and its intrinsic value - there have been few studies on the impact of selective logging on biodiversity. Therefore, this thesis - organised in three studies - aimed at characterising the response of tree diversity and composition to logging in tropical American forests.First, we drew upon the long-term forest inventories (1986-2021, trees with a diameter at breast height ≥ 10 cm) from Paracou experimental station to build a Bayesian modelling framework of tree diversity and composition trajectories after selective logging. Paracou is located in French Guiana and was disturbed by silvicultural treatments of different intensities in 1986-1987. We propagated in our Bayesian framework the uncertainty associated with botanical determination and functional trait measurements, and modelled Paracou trajectories of taxonomic, phylogenetic and functional tree diversity and composition at the species level, relatively to their pre-disturbance levels. Additionally, we assessed the effect of pre-disturbance tree community characteristics, biophysical conditions and disturbance properties on our forest attribute trajectories. Second, we used a simplified version of the aforementioned Bayesian modelling framework on long-term forest inventories from sample plots located in Costa Rica and three Amazonian countries (respectively belonging to the Observatorio de los Ecosistemas Forestales de Costa Rica and the Tropical managed Forest Observatory). We modelled their post-logging trajectories of taxonomic and functional tree diversity and composition at the genus level, from which we extracted indicators solely over the inventory timespan of each site. We then assessed the effect of pre-disturbance tree community structure and disturbance properties on such indicators. While more variable in the second study with a broader geographical scope than in the first one, we observed similar trends in both studies: diversity mostly increased after logging and tree communities mainly shifted from resource-conservative strategies to resource-acquisitive strategies. Such changes appeared to be driven by the abundant and transient recruitment of early-successional species with acquisitive trait values, which provided them with a competitive advantage as disturbance intensity - i.e., light and space availability - increased. Indeed, changes in diversity and composition increased in both studies with disturbance intensity whereas disturbance selectivity, pre-disturbance tree community characteristics and biophysical conditions had no significant effect. Third, building up on the paramount importance of disturbance intensity in the two previous studies, we developed an original Bayesian hierarchical model of recovery trajectories, considering disturbed forests in a common framework, through a disturbance intensity gradient. We tested our modelling approach on data from two long-term experiments in Costa Rica and French Guiana, set up after selective logging, agriculture, and clearcutting and fire.Overall, these results opened various perspectives on the methods used to evaluate forest response to disturbance, the forest response itself and the ecological processes underlying forest succession, and how disturbed forests could be considered in forest management and conservation plans