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Multivariate intensity estimation via hyperbolic wavelet selection

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  • Akakpo, Nathalie

Abstract

We propose a new statistical procedure that can overcome the curse of dimensionality without structural assumptions on the function to estimate. It relies on a least-squares type penalized criterion and a new collection of models built from hyperbolic biorthogonal wavelet bases. We study its properties in a unifying intensity estimation framework, where an oracle-type inequality and adaptation to mixed smoothness are shown to hold. We also show how to implement the estimator with an algorithm whose complexity is manageable.

Suggested Citation

  • Akakpo, Nathalie, 2017. "Multivariate intensity estimation via hyperbolic wavelet selection," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 32-57.
  • Handle: RePEc:eee:jmvana:v:161:y:2017:i:c:p:32-57
    DOI: 10.1016/j.jmva.2017.07.005
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    1. Kallsen, Jan & Tankov, Peter, 2006. "Characterization of dependence of multidimensional Lévy processes using Lévy copulas," Journal of Multivariate Analysis, Elsevier, vol. 97(7), pages 1551-1572, August.
    2. Song, Seongjoo, 2010. "Lévy density estimation via information projection onto wavelet subspaces," Statistics & Probability Letters, Elsevier, vol. 80(21-22), pages 1623-1632, November.
    3. Genest, Christian & Masiello, Esterina & Tribouley, Karine, 2009. "Estimating copula densities through wavelets," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 170-181, April.
    4. Autin, F. & Le Pennec, E. & Tribouley, K., 2010. "Thresholding methods to estimate copula density," Journal of Multivariate Analysis, Elsevier, vol. 101(1), pages 200-222, January.
    5. Genest, Christian & Nešlehová, Johanna, 2007. "A Primer on Copulas for Count Data," ASTIN Bulletin, Cambridge University Press, vol. 37(2), pages 475-515, November.
    6. Florian Ueltzhöfer & Claudia Klüppelberg, 2011. "An oracle inequality for penalised projection estimation of Lévy densities from high-frequency observations," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(4), pages 967-989.
    7. Comte, F. & Genon-Catalot, V., 2009. "Nonparametric estimation for pure jump Lévy processes based on high frequency data," Stochastic Processes and their Applications, Elsevier, vol. 119(12), pages 4088-4123, December.
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