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All issues Volume 17 (2013) ESAIM: PS, 17 (2013) 672-697 References
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Issue
ESAIM: PS
Volume 17, 2013
Page(s) 672 - 697
DOI https://doi.org/10.1051/ps/2012017
Published online 04 November 2013
  1. N. Akakpo, Adaptation to anisotropy and inhomogeneity via dyadic piecewise polynomial selection. Math. Meth. Stat. 21 (2012) 1–28. [Google Scholar]
  2. N. Akakpo and C. Lacour, Inhomogeneous and anisotropic conditional density estimation from dependent data. Electon. J. Statist. 5 (2011) 1618–1653. [Google Scholar]
  3. A. Antoniadis, J. Bigot and R. von Sachs, A multiscale approach for statistical characterization of functional images. J. Comput. Graph. Stat. 18 (2008) 216–237. [Google Scholar]
  4. A. Barron, C. Huang, J. Li and X. Luo, MDL Principle, Penalized Likelihood, and Statistical Risk, in Festschrift in Honor of Jorma Rissanen on the Occasion of his 75th Birthday. Tampere University Press (2008). [Google Scholar]
  5. D. Bashtannyk and R. Hyndman, Bandwidth selection for kernel conditional density estimation. Comput. Stat. Data Anal. 36 (2001) 279–298. [CrossRef] [Google Scholar]
  6. L. Bertrand, M.-A. Languille, S.X. Cohen, L. Robinet, C. Gervais, S. Leroy, D. Bernard, E. Le Pennec, W. Josse, J. Doucet and S. Schöder, European research platform IPANEMA at the SOLEIL synchrotron for ancient and historical materials. J. Synchrotron Radiat. 18 (2011) 765–772. [CrossRef] [PubMed] [Google Scholar]
  7. Ch. Biernacki, G. Celeux, G. Govaert and F. Langrognet, Model-based cluster and discriminant analysis with the MIXMOD software. Comput. Stat. Data Anal. 51 (2006) 587–600. [CrossRef] [MathSciNet] [Google Scholar]
  8. L. Birgé and P. Massart, Minimum contrast estimators on sieves: exponential bounds and rates of convergence. Bernoulli 4 (1998) 329–375. [CrossRef] [MathSciNet] [Google Scholar]
  9. L. Birgé and P. Massart, Minimal penalties for gaussian model selection. Probab. Theory Related Fields 138 (2007) 33–73. [CrossRef] [MathSciNet] [Google Scholar]
  10. G. Blanchard, C. Schäfer, Y. Rozenholc and K.R. Müller, Optimal dyadic decision trees. Mach. Learn. 66 (2007) 209–241. [CrossRef] [Google Scholar]
  11. E. Brunel, F. Comte and C. Lacour, Adaptive estimation of the conditional density in presence of censoring. Sankhy 69 (2007) 734–763. [Google Scholar]
  12. S.X. Cohen and E. Le Pennec, Conditional density estimation by penalized likelihood model selection and applications. Technical report, RR-7596. INRIA (2011). arXiv:1103.2021. [Google Scholar]
  13. S.X. Cohen and E. Le Pennec, Conditional density estimation by penalized likelihood model selection. Submitted (2012). [Google Scholar]
  14. S.X. Cohen and E. Le Pennec, Unsupervised segmentation of hyperspectral images with spatialized Gaussian mixture model and model selection. Submitted (2012). [Google Scholar]
  15. J. de Gooijer and D. Zerom, On conditional density estimation. Stat. Neerlandica 57 (2003) 159–176. [CrossRef] [Google Scholar]
  16. D. Donoho, CART and best-ortho-basis: a connection. Ann. Stat. 25 (1997) 1870–1911. [Google Scholar]
  17. S. Efromovich, Conditional density estimation in a regression setting. Ann. Stat. 35 (2007) 2504–2535. [CrossRef] [Google Scholar]
  18. S. Efromovich, Oracle inequality for conditional density estimation and an actuarial example. Ann. Inst. Stat. Math. 62 (2010) 249–275. [CrossRef] [Google Scholar]
  19. J. Fan, Q. Yao and H. Tong, Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems. Biometrika 83 (1996) 189–206. [CrossRef] [Google Scholar]
  20. Ch. Genovese and L. Wasserman, Rates of convergence for the Gaussian mixture sieve. Ann. Stat. 28 (2000) 1105–1127. [CrossRef] [Google Scholar]
  21. L. Györfi and M. Kohler, Nonparametric estimation of conditional distributions. IEEE Trans. Inform. Theory 53 (2007) 1872–1879. [CrossRef] [MathSciNet] [Google Scholar]
  22. P. Hall, R. Wolff and Q. Yao, Methods for estimating a conditional distribution function. J. Amer. Stat. Assoc. 94 (1999) 154–163. [CrossRef] [Google Scholar]
  23. T. Hofmann, Probabilistic latent semantic analysis, in Proc. of Uncertainty in Artificial Intelligence (1999). [Google Scholar]
  24. Y. Huang, I. Pollak, M. Do and C. Bouman, Fast search for best representations in multitree dictionaries. IEEE Trans. Image Process. 15 (2006) 1779–1793. [CrossRef] [PubMed] [Google Scholar]
  25. R. Hyndman and Q. Yao, Nonparametric estimation and symmetry tests for conditional density functions. J. Nonparam. Stat. 14 (2002) 259–278. [Google Scholar]
  26. R. Hyndman, D. Bashtannyk and G. Grunwald, Estimating and visualizing conditional densities. J. Comput. Graphical Stat. 5 (1996) 315–336. [Google Scholar]
  27. B. Karaivanov and P. Petrushev, Nonlinear piecewise polynomial approximation beyond besov spaces. Appl. Comput. Harmonic Anal. 15 (2003) 177–223. [CrossRef] [MathSciNet] [Google Scholar]
  28. E. Kolaczyk and R. Nowak, Multiscale generalised linear models for nonparametric function estimation. Biometrika 92 (2005) 119–133. [CrossRef] [Google Scholar]
  29. E. Kolaczyk, J. Ju and S. Gopal, Multiscale, multigranular statistical image segmentation. J. Amer. Stat. Assoc. 100 (2005) 1358–1369. [Google Scholar]
  30. Q. Li and J. Racine, Nonparametric Econometrics: Theory and Practice. Princeton University Press (2007). [Google Scholar]
  31. J. Lin, Divergence measures based on the Shannon entropy. IEEE Trans. Inform. Theory 37 (1991) 145–151. [Google Scholar]
  32. P. Massart, Concentration inequalities and model selection, vol. 1896 of Lecture Notes in Mathematics (2007). Lectures from the 33rd Summer School on Probability Theory held in Saint-Flour (2003), With a foreword by Jean Picard. [Google Scholar]
  33. C. Maugis and B. Michel, A non asymptotic penalized criterion for Gaussian mixture model selection. ESAIM: PS 15 (2012) 41–68. [Google Scholar]
  34. C. Maugis and B. Michel, Data-driven penalty calibration: a case study for Gaussian mixture model selection. ESAIM: PS 15 (2012) 320–339. [CrossRef] [EDP Sciences] [Google Scholar]
  35. M. Rosenblatt, Conditional probability density and regression estimators, in Multivariate Analysis II, Proc. of Second Internat. Sympos., Dayton, Ohio, 1968. Academic Press (1969) 25–31. [Google Scholar]
  36. L. Si and R. Jin, Adjusting mixture weights of gaussian mixture model via regularized probabilistic latent semantic analysis, in Advances in Knowledge Discovery and Data Mining (2005) 218–252. [Google Scholar]
  37. Ch. Stone, The use of polynomial splines and their tensor products in multivariate function estimation. Ann. Stat. 22 (1994) 118–171. [CrossRef] [Google Scholar]
  38. S. Szarek, Metric entropy of homogeneous spaces, in Quantum Probability (Gdansk 1997) (1998) 395–410. [Google Scholar]
  39. S. van de Geer, The method of sieves and minimum contrast estimators. Math. Methods Stat. 4 (1995) 20–38. [Google Scholar]
  40. A. van der Vaart and J. Wellner, Weak Convergence. Springer (1996). [Google Scholar]
  41. I. van Keilegom and N. Veraverbeke, Density and hazard estimation in censored regression models. Bernoulli 8 (2002) 607–625. [MathSciNet] [Google Scholar]
  42. R. Willett and R. Nowak, Multiscale poisson intensity and density estimation. IEEE Trans. Inform. Theory 53 (2007) 3171–3187. [Google Scholar]
  43. D. Young and D. Hunter, Mixtures of regressions with predictor-dependent mixing proportions. Comput. Stat. Data Anal. 54 (2010) 2253–2266. [CrossRef] [Google Scholar]

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