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Bibliographie 165Neurology 60, 1777–81.Bauer, D. J. et Curran, P. J. (2003a). Distributional assumptions of growth mixturemodels : implications for overextraction of latent trajectory classes. PsycholMethods 8, 338–63.Bauer, D. J. et Curran, P. J. (2003b). Overextraction of latent trajectory classes :much ado about nothing ? Reply to Rindskopf (2003), Muthén (2003) and Cudeckand Henly (2003). Psychol Methods 8, 384–393.Beckett, L. A., Tancredi, D. J. et Wilson, R. S. (2004). Multivariate longitudinalmodels for complex change processes. Stat Med 23, 231–9.Benton, A. (1965). Manuel pour l’application du Test de Rétention Visuelle. Applicationscliniques et expérimentales. Centre de Psychologie appliquée, Paris,2ème édition française edition.Berchtold, A. (2004). Optimization of mixture models : comparison of differentstrategies. Computational statistics 19, 385–406.Beunckens, C., Molenberghs, G., Verbeke, G. et Mallinckrodt, C. (2006). A latentclassmixture model for incomplete longitudinal gaussian data. Center for statistics,Hasselt University, Belgium.Biernacki, C., Celeux, G. et Govaert, G. (2003). Choosing starting values for theEM algorithm for getting the highest likelihood in multivariate Gaussian mixturemodels. Computational Statistics and Data Analysis 41, 561–575.Bohning, D. (1995). A review of reliable maximum likelihood algorithms for semiparametricmixture models. Journal of Statistical Planning and Inference 47,5–28.Bohning, D. (2000). Computer-assisted analysis of mixtures and applications : metaanalysis,disease mapping and others. Chapmann & Hall/CRC, Boca Raton, FL.Bohning, D. et Seidel, W. (2003). Editorial : recent developments in mixture models.Computational Statistics and Data Analysis 41, 349–357.Brooks, J. O. et Yesavage, J. A. (1995). Identification of fast and slow decliners inAlzheimer disease : a different approach. Alzheimer Dis Assoc Disord 9 Suppl