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All issues Volume 10 (September 2006) ESAIM: PS, 10 (2006) 24-45 References
Free Access
Issue
ESAIM: PS
Volume 10, September 2006
Page(s) 24 - 45
DOI https://doi.org/10.1051/ps:2006001
Published online 31 January 2006
  1. H. Akaike, A new look at the statistical model identification. IEEE Trans. Automatic Control 19 (1974) 716–723. [CrossRef] [Google Scholar]
  2. A.R. Barron, L. Birgé and P. Massart. Risk bounds for model selection via penalization. Probab. Theory Relat. Fields 113 (1999) 301–415. [CrossRef] [MathSciNet] [Google Scholar]
  3. L. Birgé and P. Massart, From model selection to adaptive estimation, in Festschrift for Lucien Le Cam: Research Papers in Probability and Statistics, D. Pollard, E. Torgersen and G. Yang, Eds., Springer-Verlag, New York (1997) 55–87. [Google Scholar]
  4. L. Birgé and P. Massart, Gaussian model selection. J. Eur. Math. Soc. 3 (2001) 203–268. [CrossRef] [MathSciNet] [Google Scholar]
  5. G. Castellan, Modified Akaike's criterion for histogram density estimation. Technical Report. Université Paris-Sud, Orsay (1999). [Google Scholar]
  6. G. Castellan, Sélection d'histogrammes à l'aide d'un critère de type Akaike. CRAS 330 (2000) 729–732. [Google Scholar]
  7. J. Daly, The construction of optimal histograms. Commun. Stat., Theory Methods 17 (1988) 2921–2931. [Google Scholar]
  8. L. Devroye, A Course in Density Estimation. Birkhäuser, Boston (1987). [Google Scholar]
  9. L. Devroye, and L. Györfi, Nonparametric Density Estimation: The L1 View. John Wiley, New York (1985). [Google Scholar]
  10. L. Devroye and G. Lugosi, Combinatorial Methods in Density Estimation. Springer-Verlag, New York (2001). [Google Scholar]
  11. D. Freedman and P. Diaconis, On the histogram as a density estimator: L2 theory. Z. Wahrscheinlichkeitstheor. Verw. Geb. 57 (1981) 453–476. [Google Scholar]
  12. P. Hall, Akaike's information criterion and Kullback-Leibler loss for histogram density estimation. Probab. Theory Relat. Fields 85 (1990) 449–467. [CrossRef] [Google Scholar]
  13. P. Hall and E.J. Hannan, On stochastic complexity and nonparametric density estimation. Biometrika 75 (1988) 705–714. [CrossRef] [MathSciNet] [Google Scholar]
  14. K. He and G. Meeden, Selecting the number of bins in a histogram: A decision theoretic approach. J. Stat. Plann. Inference 61 (1997) 49–59. [CrossRef] [Google Scholar]
  15. D.R.M. Herrick, G.P. Nason and B.W. Silverman, Some new methods for wavelet density estimation. Sankhya, Series A 63 (2001) 394–411. [Google Scholar]
  16. M.C. Jones, On two recent papers of Y. Kanazawa. Statist. Probab. Lett. 24 (1995) 269–271. [CrossRef] [MathSciNet] [Google Scholar]
  17. Y. Kanazawa, Hellinger distance and Akaike's information criterion for the histogram. Statist. Probab. Lett. 17 (1993) 293–298. [CrossRef] [MathSciNet] [Google Scholar]
  18. L.M. Le Cam, Asymptotic Methods in Statistical Decision Theory. Springer-Verlag, New York (1986). [Google Scholar]
  19. L.M. Le Cam and G.L. Yang, Asymptotics in Statistics: Some Basic Concepts. Second Edition. Springer-Verlag, New York (2000). [Google Scholar]
  20. J. Rissanen, Stochastic complexity and the MDL principle. Econ. Rev. 6 (1987) 85–102. [CrossRef] [Google Scholar]
  21. M. Rudemo, Empirical choice of histograms and kernel density estimators. Scand. J. Statist. 9 (1982) 65–78. [MathSciNet] [Google Scholar]
  22. D.W. Scott, On optimal and databased histograms. Biometrika 66 (1979) 605–610. [CrossRef] [MathSciNet] [Google Scholar]
  23. H.A. Sturges, The choice of a class interval. J. Am. Stat. Assoc. 21 (1926) 65–66. [Google Scholar]
  24. C.C. Taylor, Akaike's information criterion and the histogram. Biometrika. 74 (1987) 636–639. [CrossRef] [MathSciNet] [Google Scholar]
  25. G.R. Terrell, The maximal smoothing principle in density estimation. J. Am. Stat. Assoc. 85 (1990) 470–477. [CrossRef] [Google Scholar]
  26. M.P. Wand, Data-based choice of histogram bin width. Am. Statistician 51 (1997) 59–64. [CrossRef] [Google Scholar]

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