E. Mise and . Du, 17 et 18 sur les trois variables latentes. En rouge, le score 'nul' de référence. 5.2

M. En and O. Bipea, Figure 5.6 Répartition de la position relative des scores, sommés sur les trois variables latentes, de six laboratoires sur les itérations de l'algorithme PX-Gibbs. Le nuage bleu représente les valeurs bruitées de la fonction de répartition empirique. 5.2, MISE

M. En, O. Et-chib-]-albert, J. H. Et-chib, and S. , Bayesian analysis of binary and polychotomous response data, Journal of the American Statistical Association, issue.422, p.88669679, 1993.

D. Amarouche, S. Amarouche, and M. Et-désenfant, Formulaire pour le calcul d'une valeur de référence dans les comparaisons clés internationales (KCRV) Rapport technique, 2004.

. Beichl, . Sullivan, I. Beichl, and F. Et-sullivan, The Metropolis Algorithm, Computing in Science & Engineering, vol.2, issue.1, p.6569, 2000.
DOI : 10.1109/5992.814660

. Bipm, Evaluation des données de mesure Supplément 1 du 'Guide pour l'expression de l'incertitude de mesure' propagation de distributions par une méthode de monte carlo, Joint Committee for Guides in Metrology JCGM, vol.101, 2008.

. Bipm, Guide pour l'expression de l'incertitude de mesure, ISO/IEC Guide International Organization for Standardization, vol.98, 1995.

G. E. Box, Non-normality and tests on variances, Biometrika, vol.40, p.318335, 1953.

C. Et-greenberg-]-chib, S. Et-greenberg, and E. , Understanding the Metropolis-Hastings algorithm, Journal of the American Statistical Association, issue.412, pp.85-972985, 1990.

. Chunovkina, Analysis of key comparison data and laboratory biases, Metrologia, vol.45, issue.2, pp.45211-216, 2008.
DOI : 10.1088/0026-1394/45/2/010

P. Congdon, Applied Bayesian modelling, 2003.
DOI : 10.1002/9781118895047

M. G. Cox, The evaluation of key comparison data: determining the largest consistent subset, Metrologia, vol.44, issue.3, p.44187200, 2007.
DOI : 10.1088/0026-1394/44/3/005

. Dempster, Maximum likelihood estimation from incomplete data via the EM algorithm "with discussion, Journal of the Royal Statistical Society, Serie B, vol.39, issue.1, p.138, 1977.

R. Eckhardt, Stan Ulam, John von Neumann, and the Monte Carlo method, BIBLIOGRAPHIE Los Alamos Science, issue.15, p.125130, 1987.

. Elster, Draft GUM Supplement 1 and Bayesian analysis, Metrologia, vol.44, issue.3, pp.44-75, 2007.
DOI : 10.1088/0026-1394/44/3/N03

. Foulley, J. Foulley, and F. Et-jaffrézic, Modelling and estimating heterogeneous variances in threshold models for ordinal discrete data via Winbugs/Openbugs, Computer Methods and Programs in Biomedicine, vol.97, issue.1, p.1927, 2010.
DOI : 10.1016/j.cmpb.2009.05.004
URL : https://hal.archives-ouvertes.fr/hal-01193452

. Gelman, Bayesian Data Analysis, 2004.

. Gelman, . Hill, A. Gelman, and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, 2006.
DOI : 10.1017/CBO9780511790942
URL : https://repozitorij.uni-lj.si/Dokument.php?id=69512

. Gelman, Posterior predictive assessment of model tness via realized discrepancies, Statistica Sinica, vol.6, p.733807, 1996.

G. Et-geman-]-geman, S. Et-geman, and D. , Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, p.721741, 1984.

G. Et-dunson-]-ghosh, J. Et-dunson, and D. B. , Default prior distributions and ecient posterior computation in Bayesian factor analysis, Journal of Computational and Graphical Statistics, vol.18, issue.2, p.306320, 2009.

. Graybill, . Deal, F. A. Graybill, and R. B. Et-deal, Combining Unbiased Estimators, Biometrics, vol.15, issue.4, p.543550, 1959.
DOI : 10.2307/2527652

W. K. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, p.97109, 1970.
DOI : 10.1093/biomet/57.1.97

R. Huber, P. Huber, and E. Et-ronchetti, Robust Statistics, 2009.
DOI : 10.1002/0471725250

. Iso-casco, Exigences générales concernant la compétence des laboratoires d'étalonnage et d'essais. ISO/IEC 17025, International Organization for Standardization, 2005.

E. Jakobowicz, Contributions aux modèles d'équations structurelles à variables latentes, Thèse de doctorat, Conservatoire National des Arts et Métiers, 2007.

J. Et-derquenne-]-jakobowicz, E. Et-derquenne, and C. , A modied PLS Path Modeling algorithm handling reective categorical variables and a new model building strategy, Computational Statistics and Data Analysis, issue.7, p.5136663678, 2007.

. Jasra, MCMC and the label switching problem in Bayesian mixture models Vocabulaire international de métrologie concepts fondamentaux et généraux et termes associés, Statistical Science JCGM ISO/IEC Guide International Organization for Standardization (ISO), vol.20, issue.99, 2005.

K. Kafadar, John Tukey and Robustness, Statistical Science, vol.18, issue.3, p.319331, 2003.
DOI : 10.1214/ss/1076102419
URL : http://projecteuclid.org/download/pdf_1/euclid.ss/1076102419

. Lawrence, Bayesian Inference for Multivariate Ordinal Data Using Parameter Expansion, Technometrics, vol.50, issue.2, p.50182191, 2008.
DOI : 10.1198/004017008000000064

S. Lee, Structural Equation Modelling : A Bayesian Approach, 2007.

. Lewandowski, Parameter expansion and ecient inference, Statistical Science, 2011.
DOI : 10.1214/10-sts348
URL : http://arxiv.org/abs/1104.2407

S. Lindley, D. V. Lindley, and A. F. Smith, Bayes estimates for the linear model, Journal of the Royal Statistical Society. Series B (Methodological), vol.34, issue.1, p.141, 1972.

. Liu, Parameter expansion to accelerate EM: the PX-EM algorithm, Biometrika, vol.85, issue.4, p.755770, 1998.
DOI : 10.1093/biomet/85.4.755

J. S. Liu, Monte Carlo strategies in scientic computing, 2001.

. Liu, Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes, Biometrika, vol.81, issue.1, p.812740, 1994.
DOI : 10.1093/biomet/81.1.27

. Liu, . Wu, J. S. Liu, and Y. N. Et-wu, Parameter Expansion for Data Augmentation, Journal of the American Statistical Association, vol.17, issue.448, p.9412641274, 1999.
DOI : 10.1002/9780470316696
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.44.5748

P. Mandel, J. Mandel, and R. C. Et-paule, Interlaboratory evaluation of a material with unequal numbers of replicates, Analytical chemistry, issue.11, p.42, 1970.

M. , R. Marin, J. Et-robert, and C. P. , Bayesian Core : a practical approach to computational Bayesian statistics, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00450489

P. Mccullagh, Regression models for ordinal data, Journal of the Royal Statistical Society. Series B (Methodological), vol.42, issue.2, p.109142, 1980.

N. Metropolis, The beginning of the Monte Carlo method, Los Alamos Science, issue.15, p.125130, 1987.

. Metropolis, Equations of state calculations by fast computing machines The Monte Carlo method, J.Chem.Phys Journal of the American Statistical Association, vol.21, issue.247, pp.10871092-44335341, 1949.

. Meza, Estimation in the probit normal model for binary outcomes using the SAEM algorithm, Computational Statistics & Data Analysis, vol.53, issue.4, p.13501360, 2009.
DOI : 10.1016/j.csda.2008.11.024

J. W. Müller, Possible advantages of a robust evaluation of comparisons, Journal of Research of the National Institute of Standards and Technology, vol.105, issue.4, p.551555, 2000.
DOI : 10.6028/jres.105.044

. Palomo, Bayesian structural equation modeling, p.163188, 2007.

. Parent, . Bernier, E. Parent, and J. Et-bernier, Le raisonnement bayésien. Modélisation et inférence, 2007.

C. P. Robert, The Bayesian choice, 2001.
DOI : 10.1007/978-1-4757-4314-2

R. , C. Robert, C. P. Et-casella, and G. , Monte Carlo statistical methods, 2004.

J. Rosenthal, W.K. Hastings, statistician and developer of the Metropolis-Hastings algorithm, 2004.

P. J. Rousseeuw, Tutorial to robust statistics, Journal of Chemometrics, vol.85, issue.1, p.120, 1991.
DOI : 10.1002/cem.1180050103

A. L. Rukhin, Weighted means statistics in interlaboratory studies, Metrologia, vol.46, issue.3, p.323331, 2009.
DOI : 10.1088/0026-1394/46/3/021

R. Skrondal, A. Skrondal, and S. Et-rabe-hesketh, Generalized latent variable modeling, 2004.

. Tanner, . Wong, M. A. Tanner, and W. H. Et-wong, The Calculation of Posterior Distributions by Data Augmentation, Journal of the American Statistical Association, vol.56, issue.398, p.82528540, 1987.
DOI : 10.1016/0304-4076(84)90007-1

. Tenenhaus, PLS path modeling, Computational Statistics & Data Analysis, vol.48, issue.1, p.159205, 2005.
DOI : 10.1016/j.csda.2004.03.005
URL : https://hal.archives-ouvertes.fr/hal-00869374

B. Toman, Bayesian Approaches to Calculating a Reference Value in Key Comparison Experiments, Technometrics, vol.49, issue.1, p.8187, 2007.
DOI : 10.1198/004017006000000273

T. Et-possolo-]-toman, B. Et-possolo, and A. , Laboratory eects models for interlaboratory comparisons, Accred Qual Assur, vol.14, p.553563, 2009.

. Tukey and J. W. Tukey, Discussion of Anscombe and Daniel papers, Technometrics, issue.2, p.159163, 1960.

. Tukey and J. W. Tukey, A survey of sampling from contaminated distributions The art of data augmentation, Journal of Computational and Graphical Statistics, vol.10, issue.1, p.150, 1960.

W. Et-wöger-]-weise, K. Et-wöger, and W. , A Bayesian theory of measurement uncertainty, Meas. Sci. Technol, vol.4, issue.1, p.111, 1993.

W. Et-wöger-]-weise, K. Et-wöger, and W. , Comparison of two measurement results using the Bayesian theory of measurement uncertainty, Meas. Sci. Technol, vol.5, issue.3, p.879882, 1994.

N. F. Zhang, The uncertainty associated with the weighted mean of measurement data, Metrologia, vol.43, issue.3, 2006.
DOI : 10.1088/0026-1394/43/3/002