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P. Massart 319ReferencesAkaike, H. (1973). Information theory and an extension of the maximumlikelihood principle. Proceedings of the Second International Symposiumon Information Theory. Akademia Kiado, Budapest, pp. 267–281.Arlot, S. and Massart, P. (2009). Data driven calibration of penalties for leastsquares regression. Journal of Machine Learning Research, 10:245–279.Bahadur, R.R. (1958). Examples of inconsistency of maximum likelihoodestimates. Sankhyā, SeriesA,20:207–210.Barron, A.R., Birgé, L., and Massart, P. (1999). Risk bounds for modelselection via penalization. Probability Theory and Related Fields,113:301–413.Barron, A.R. and Cover, T.M. (1991). Minimum complexity density estimation.IEEE Transactions on Information Theory, 37:1034–1054.Birgé, L. and Massart, P. (1993). Rates of convergence for minimum contrastestimators. Probability Theory and Related Fields, 97:113–150.Birgé, L. and Massart, P. (1997). From model selection to adaptive estimation.In Festschrift for Lucien Le Cam: Research Papers in Probabilityand Statistics (D. Pollard, E. Torgersen, and G. Yang, Eds.). Springer,New York, pp. 55–87.Birgé, L. and Massart, P. (2007). Minimal penalties for Gaussian model selection.Probability Theory and Related Fields, 138:33–73.Borell, C. (1975). The Brunn–Minkowski inequality in Gauss space. InventionesMathematicae, 30:207–216.Boucheron, S., Bousquet, O., Lugosi, G., and Massart, P. (2005). Momentinequalities for functions of independent random variables. The Annalsof Probability, 33:514–560.Boucheron, S., Lugosi, G., and Massart, P. (2000). A sharp concentrationinequality with applications. Random Structures and Algorithms, 16:277–292.Boucheron, S., Lugosi, G., and Massart, P. (2003). Concentration inequalitiesusing the entropy method. The Annals of Probability, 31:1583–1614.Boucheron, S., Lugosi, G., and Massart, P. (2013). Concentration Inequalities:A Nonasymptotic Theory of Independence. Oxford University Press,Oxford, UK.

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