Academic literature on the topic 'Modèle à variables latentes'
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Journal articles on the topic "Modèle à variables latentes"
Lacroix, Robert, Claude Montmarquette, Sophie Mahseredjian, and Nicole Froment. "Disparités interindustrielles dans les taux de départs volontaires : une étude empirique." Articles 67, no. 4 (February 27, 2009): 458–81. http://dx.doi.org/10.7202/602049ar.
Full textAZHARI, Amine, and Si Mohamed BOUAZIZ. "La Relation Entre La Qualité De L'information Comptable Et L'organisation Du Service Qui La Produit : Une Etude Empirique Dans Le Contexte Agricole Marocain." International Journal of Financial Accountability, Economics, Management, and Auditing (IJFAEMA) 3, no. 5 (September 28, 2021): 780–808. http://dx.doi.org/10.52502/ijfaema.v3i5.153.
Full textGirard, Stéphanie, and Sébastien Béland. "Analyser l’interaction de variables latentes : une exemplification méthodologique de la méthode d’équations structurelles avec interaction latente1." Revue des sciences de l’éducation 43, no. 3 (August 22, 2018): 28–60. http://dx.doi.org/10.7202/1050972ar.
Full textJacquinot, Pascal, and F. Mihoubi. "Dynamique et hétérogénéité de l’emploi en déséquilibre." Articles 72, no. 2 (February 13, 2009): 113–48. http://dx.doi.org/10.7202/602200ar.
Full textMassiera, Philippe, Laura Trinchera, and Giorgio Russolillo. "Evaluation de la présence des capacités marketing: Proposition d’un index multidimensionnel et hiérarchique." Recherche et Applications en Marketing (French Edition) 33, no. 1 (December 11, 2017): 31–55. http://dx.doi.org/10.1177/0767370117741108.
Full textCorral Verdugo, Victor. "El significado de "variables latentes" en psicología." ACTA COMPORTAMENTALIA 9, no. 1 (June 1, 2001): 85–98. http://dx.doi.org/10.32870/ac.v9i1.14634.
Full textPoza Lara, Carlos. "Técnicas estadísticas multivariantes para la generación de variables latentes." Revista EAN, no. 64 (August 1, 2008): 89. http://dx.doi.org/10.21158/01208160.n64.2008.454.
Full textBurgos, José E. "Comentario. Variables latentes, conceptos y definiciones." ACTA COMPORTAMENTALIA 9, no. 2 (December 1, 2001): 251–75. http://dx.doi.org/10.32870/ac.v9i2.14640.
Full textCorral Verdugo, Víctor. "Modelos de variables latentes para la investigación conductual." ACTA COMPORTAMENTALIA 3, no. 2 (December 1, 1995): 171–90. http://dx.doi.org/10.32870/ac.v3i2.18319.
Full textPlevoets, Koen. "Lectometry and Latent Variables: a Model for Underlying Determinants of (Normative) Choices in Written and Audiovisual Translations." Zeitschrift für Dialektologie und Linguistik 87, no. 2 (2020): 144. http://dx.doi.org/10.25162/zdl-2020-0006.
Full textDissertations / Theses on the topic "Modèle à variables latentes"
Podosinnikova, Anastasia. "Sur la méthode des moments pour l'estimation des modèles à variables latentes." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEE050/document.
Full textLatent linear models are powerful probabilistic tools for extracting useful latent structure from otherwise unstructured data and have proved useful in numerous applications such as natural language processing and computer vision. However, the estimation and inference are often intractable for many latent linear models and one has to make use of approximate methods often with no recovery guarantees. An alternative approach, which has been popular lately, are methods based on the method of moments. These methods often have guarantees of exact recovery in the idealized setting of an infinite data sample and well specified models, but they also often come with theoretical guarantees in cases where this is not exactly satisfied. In this thesis, we focus on moment matchingbased estimation methods for different latent linear models. Using a close connection with independent component analysis, which is a well studied tool from the signal processing literature, we introduce several semiparametric models in the topic modeling context and for multi-view models and develop moment matching-based methods for the estimation in these models. These methods come with improved sample complexity results compared to the previously proposed methods. The models are supplemented with the identifiability guarantees, which is a necessary property to ensure their interpretability. This is opposed to some other widely used models, which are unidentifiable
Tayeb, Arafat. "Estimation bayésienne des modèles à variables latentes." Paris 9, 2006. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=2006PA090061.
Full textIn this thesis, we study some models with latent variables. Given a set of data , we suppose that there is a hidden variable such that the distribution of conditional on is of known class and is often depending on a (multidimensional) parameter. This parameter can depend on time and on the latent variable. When does not depend on , we simply write. Depending on the model, the variable represents the observation allocation, the observation component, the observation state or its regime. The aim of this work is to estimate the parameter and the hidden variable. Bayesian inference about the parameter is given by its posterior distribution. Precisely, we wish either to produce an efficient sample (approximately) following this distribution or to approximate some of its properties like mean, median or modes. Different methods of sampling and/or deriving of such posterior properties are used in this thesis. Mostly, five models are studied and for each situation, specific techniques are used
Matias, Catherine. "Statistique asymptotique dans des modèles à variables latentes." Habilitation à diriger des recherches, Université d'Evry-Val d'Essonne, 2008. http://tel.archives-ouvertes.fr/tel-00349639.
Full textMa présentation s'organise en trois grandes thématiques : les travaux portant sur des séquences, notamment sur la modélisation de leur distribution et des processus d'évolution sous-jacents ; les travaux de statistique semi ou non paramétrique portant sur des signaux observés avec du bruit ; et enfin les travaux (en partie en cours) portant sur les graphes aléatoires.
Jakobowicz, Emmanuel. "Contributions aux modèles d'équations structurelles à variables latentes." Phd thesis, Conservatoire national des arts et metiers - CNAM, 2007. http://tel.archives-ouvertes.fr/tel-00207990.
Full textBry, Xavier. "Une méthodologie exploratoire pour l'analyse et la synthèse d'un modèle explicatif : l'Analyse en Composantes Thématiques." Paris 9, 2004. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=2004PA090055.
Full textDortet-Bernadet, Vincent. "Contribution à l'étude statistique de modèles à variables latentes." Toulouse 3, 2001. http://www.theses.fr/2001TOU30135.
Full textGuyon, Hervé. "Variables latentes et processus mentaux : une réflexion épistémologique et méthodologique." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB015/document.
Full textMy thesis considers that experimental psychology must clarify its epistemological position to clarify the formal validation of its approach, without necessarily having to refer to the framework of Science Physics. From a critical reflection, I propose to shift the epistemological framework in psychology and clearly pose a pragmatic-realistic framework. The main thesis of this work is: 1 / mental properties must be understood as emerging phenomena, which implies that their analysis can not be done nor at the neuronal level, nor at the internal dynamics of cognitive processes, but necessarily at these emerging phenomena; 2 / to analyze the mental properties as emerging forms, psychometrics need to use concepts that are in permanent tension between objectivity and intersubjectivity; accordingly, psychometrics must assert a pragmatic-realist approach, breaking with classical empiricism-realistic; 3 / a pragmatist-realistic approach, based among other things on the abduction, can overcome the contradictions pointed in the academic literature on mental properties and their measurements; 4 / a framework for measuring mental properties by latent variables becomes possible if the framework is also understood as a pragmatic-realist; 5 / but use realistic-pragmatic returns accordingly critical of both models with latent variables developed in the academic literature and the social uses of these models. The second part of my thesis focuses on a specific part of formalization of latent variables: the formative model. I develop Monte Carlo simulations to check the range of parameters for efficient formative measure as part of a realistic-empirical positioning
Bock, Dumas Élodie de. "Identification de stratégies d’analyse de variables latentes longitudinales en présence de données manquantes potentiellement informatives." Nantes, 2014. http://archive.bu.univ-nantes.fr/pollux/show.action?id=ed3dcb7e-dec1-4506-b99d-50e3448d1ce4.
Full textThe purpose of this study was to identify the most adequate strategy to analyse longitudinal latent variables (patient reported outcomes) when potentially informative missing data are observed. Models coming from classical test theory and Rasch-family were compared. In order to obtain an objective comparison of these methods, simulation studies were used. Moreover, illustrative examples were analysed. This research work showed that the method that comes from Rasch-family models performs better than the other in some circumstances, mainly for power. However, limitations were highlighted. Moreover, some results were obtained about personal mean score imputation
Casarin, Roberto. "Méthodes de simulation pour l'estimation bayésienne des modèles à variables latentes." Paris 9, 2007. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=2007PA090056.
Full textLatent variable models are now very common in econometrics and statistics. This thesis mainly focuses on the use of latent variables in mixture modelling, time series analysis and continuous time models. We follow a Bayesian inference framework based on simulation methods. In the third chapter we propose alfa-stable mixtures in order to account for skewness, heavy tails and multimodality in financial modelling. Chapter four proposes a Markov-Switching Stochastic-Volatility model with a heavy-tail observable process. We follow a Bayesian approach and make use of Particle Filter, in order to filter the state and estimate the parameters. Chapter five deals with the parameter estimation and the extraction of the latent structure in the volatilities of the US business cycle and stock market valuations. We propose a new regularised SMC procedure for doing Bayesian inference. In chapter six we employ a Bayesian inference procedure, based on Population Monte Carlo, to estimate the parameters in the drift and diffusion terms of a stochastic differential equation (SDE), from discretely observed data
Batardière, Bastien. "Machine learning for multivariate analysis of high-dimensional count data." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM047.
Full textThis thesis deals with the modeling and analysis of high-dimensional count data through the framework of latent variable models, as well as the optimization of such models. Latent variable models have demonstrated their efficacy in modeling count data with complex dependency structures, with the Poisson Log-Normal (PLN) model serving as a prime example. However, the PLN model does not meet the characteristics of real-world count datasets, primarily due to its inability to produce a high number of zeros. We propose the Zero-Inflated PLN (ZIPLN) extension to meet these characteristics. The latter and other variants of PLN are implemented in a Python package using variational inference to maximize the log-likelihood. In the second part, we focus on the finite-sum maximization problem, a common challenge when optimizing a wide range of latent variable models. We introduce an adaptive method named AdaLVR, scaling effectively with both the dimensionality and the sample size of the dataset, designed explicitly for this finite-sum optimization problem. A theoretical analysis of AdaLVR is conducted, and the convergence rate of O(T ⁻¹) is obtained in the convex setting, where T denotes the number of iterations. In the third part, we discuss the optimization of latent variable models using Monte Carlo methods, with a particular emphasis on the PLN model. The optimization occurs in a non-convex setting and necessitates the computation of the gradient, which is expressed as an intractable integral. In this context, we propose a first-order algorithm where the gradient is estimated using self-normalized importance sampling. Convergence guarantees are obtained under certain easily verifiable assumptions despite the inherent bias in the gradient estimator. Importantly, the applicability of the convergence theorem extends beyond the scope of optimization in latent variable models. In the fourth part, we focus on the implementation of the inference for PLN models, with a particular emphasis on the details of variational inference designed for these models. In the appendix, we derive confidence intervals for the PLN model, and an extension to the ZIPLN model, integrating Principal Component Analysis, is proposed. A semi-parametric approach is also introduced. Concurrently, an analysis of a real-world genomic dataset is conducted, revealing how different types of cells in plant leaves respond to a bacterial pathogen
Books on the topic "Modèle à variables latentes"
Muthen, Linda K. Mplus: Statistical analysis with latent variables : user's guide. 4th ed. Los Angeles, CA: Muthén & Muthén, 2007.
Find full textHillmer, Matthias. Kausalanalyse makroökonomischer Zusammenhänge mit latenten Variablen: Mit einer empirischen Untersuchung des Transmissionsmechanismus monetärer Impulse. Heidelberg: Physica-Verlag, 1993.
Find full textBollen, Kenneth A. Latent curve models: A structural equation perspective. Hoboken, NJ: Wiley Interscience, 2005.
Find full textBollen, Kenneth A. Latent curve models: A structural equation perspective. Hoboken, N.J: Wiley-Interscience, 2006.
Find full text1965-, Curran Patrick J., ed. Latent curve models: A structural equation perspective. Hoboken, NJ: John Wiley & Sons, 2005.
Find full textVermunt, Jeroen K. Log-linear event history analysis: A general approach with missing data, latent variables, and unobserved heterogeneity. Tilburg: Tilburg University Press, 1996.
Find full textE, Duncan Terry, ed. An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Mahwah, N.J: L. Erlbaum Associates, 1999.
Find full textAng, Andrew. A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables. Cambridge, MA: National Bureau of Economic Research, 2001.
Find full textBartholomew, David J. Latent variable models and factor analysis. 2nd ed. London: Arnold, 1999.
Find full textBartholomew, David J. Latent variable models and factor analysis. London: C. Griffin, 1987.
Find full textBook chapters on the topic "Modèle à variables latentes"
Andreß, Hans-Jürgen, Jacques A. Hagenaars, and Steffen Kühnel. "Latente Klassenanalyse und log-lineare Modelle mit latenten Variablen." In Springer-Lehrbuch, 209–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-05693-6_4.
Full textMcGrath, Robert E. "Latent-variable models." In Quantitative models in psychology., 149–75. Washington: American Psychological Association, 2011. http://dx.doi.org/10.1037/12316-007.
Full textBishop, Christopher M. "Latent Variable Models." In Learning in Graphical Models, 371–403. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5014-9_13.
Full textBeaujean, A. Alexander, and Grant B. Morgan. "Latent Variable Models." In Human–Computer Interaction Series, 233–50. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26633-6_10.
Full textTomczak, Jakub M. "Latent Variable Models." In Deep Generative Modeling, 57–127. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93158-2_4.
Full textTomczak, Jakub M. "Latent Variable Models." In Deep Generative Modeling, 93–167. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-64087-2_5.
Full textMarano, Giovanni, Gianni Betti, and Francesca Gagliardi. "Latent Class Markov Models for Measuring Longitudinal Fuzzy Poverty." In Advances in Latent Variables, 73–81. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/10104_2014_4.
Full textBergsma, Wicher, Marcel Croon, and Jacques A. Hagenaars. "Marginal modeling with latent variables." In Marginal Models, 191–221. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/b12532_6.
Full textVermunt, Jeroen K. "Longitudinal Research Using Mixture Models." In Longitudinal Research with Latent Variables, 119–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11760-2_4.
Full textArminger, Gerhard, and Franz Müller. "Statische Modelle mit latenten Variablen." In Lineare Modelle zur Analyse von Paneldaten, 89–119. Wiesbaden: VS Verlag für Sozialwissenschaften, 1990. http://dx.doi.org/10.1007/978-3-322-88758-0_6.
Full textConference papers on the topic "Modèle à variables latentes"
Gaebert, Carl, and Ulrike Thomas. "Generating Dual-Arm Inverse Kinematics Solutions using Latent Variable Models." In 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids), 373–80. IEEE, 2024. https://doi.org/10.1109/humanoids58906.2024.10769854.
Full textMelero, Gustavo García. "Incorporación de atributos intangibles en modelos de elección discreta." In CIT2016. Congreso de Ingeniería del Transporte. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/cit2016.2016.4142.
Full textGiesen, Joachim, Paul Kahlmeyer, Sören Laue, Matthias Mitterreiter, Frank Nussbaum, Christoph Staudt, and Sina Zarrieß. "Method of Moments for Topic Models with Mixed Discrete and Continuous Features." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/333.
Full textJiang, Xiubao, Xinge You, Yi Mou, Shujian Yu, and Wu Zeng. "Gaussian latent variable models for variable selection." In 2014 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, 2014. http://dx.doi.org/10.1109/spac.2014.6982714.
Full textDash, Tapas R., and Siphat Lim. "Identifying Factors Influencing Knowledge Collaboration Effects in Knowledge Alliances in Cambodia: A Structural Equation Model." In ACBSP Region 10 Annual Conference 2023. CamEd Business School, 2023. http://dx.doi.org/10.62458/camed/oar/acbsp/7-16.
Full textLuo, Yin-Jyun, Sebastian Ewert, and Simon Dixon. "Towards Robust Unsupervised Disentanglement of Sequential Data — A Case Study Using Music Audio." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/458.
Full textUrtasun, Raquel, David J. Fleet, Andreas Geiger, Jovan Popović, Trevor J. Darrell, and Neil D. Lawrence. "Topologically-constrained latent variable models." In the 25th international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390156.1390292.
Full textWillems, J. C., and J. W. Nieuwenhuis. "Continuity of latent variable models." In 29th IEEE Conference on Decision and Control. IEEE, 1990. http://dx.doi.org/10.1109/cdc.1990.203519.
Full textDikmen, Onur, and A. Taylan Cemgil. "Score matching for models with latent variables." In 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU). IEEE, 2011. http://dx.doi.org/10.1109/siu.2011.5929772.
Full textKattenbeck, Markus, and David Elsweiler. "Estimating Models Combining Latent and Measured Variables." In the 2018 Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3176349.3176899.
Full textReports on the topic "Modèle à variables latentes"
Mislevy, Robert J., and Kathleen M. Sheehan. The Information Matrix in Latent-Variable Models. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada196609.
Full textAnandkumar, Anima, Rong Ge, Daniel Hsu, Sham M. Kakade, and Matus Telgarsky. Tensor Decompositions for Learning Latent Variable Models. Fort Belvoir, VA: Defense Technical Information Center, December 2012. http://dx.doi.org/10.21236/ada604494.
Full textGertler, Paul. A Latent Variable Model of Quality Determination. Cambridge, MA: National Bureau of Economic Research, October 1985. http://dx.doi.org/10.3386/w1750.
Full textZhang, Zhen. From CFA to SEM with Moderated Mediation in Mplus. Instats Inc., 2022. http://dx.doi.org/10.61700/e6lwwzg27rqsr469.
Full textCollins, David H. Jr. Latent Variable Models for Quantification of Margins and Uncertainties. Office of Scientific and Technical Information (OSTI), July 2013. http://dx.doi.org/10.2172/1088891.
Full textZyphur, Michael. From CFA to SEM with Moderated Mediation in R. Instats Inc., 2022. http://dx.doi.org/10.61700/75sjvfs0ve1d4469.
Full textZyphur, Michael. From CFA to SEM with Moderated Mediation in Mplus. Instats Inc., 2022. http://dx.doi.org/10.61700/a6tru90pc9miu469.
Full textZyphur, Michael. From CFA to SEM with Moderated Mediation in R (Free On-Demand Seminar). Instats Inc., 2022. http://dx.doi.org/10.61700/xria1if8u3nip469.
Full textZyphur, Michael. Intermediate SEM in Stata: From CFA to SEM. Instats Inc., 2022. http://dx.doi.org/10.61700/9qo0ssbbzp4nl469.
Full textRaykov, Tenko. Latent Class Analysis and Mixture Modeling. Instats Inc., 2023. http://dx.doi.org/10.61700/tkd5fah8evykd469.
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