Open Access
Volume 25, 2021
Page(s) 1 - 30
Published online 04 March 2021
  1. A. Bernacchia and S. Pigolotti, Self-consistent method for density estimation. J. Royal Stat. Soc. 73 (2011) 407–422. [Google Scholar]
  2. D. Bosq, Nonparametric statistics for stochastic processes: estimation and prediction. In Vol. 10. Springer Science & Business Media (2012). [Google Scholar]
  3. J.W. Cooley and J.W. Tukey, An algorithm for the machine calculation of complex Fourier series. Math. Comput. 19 (1965) 297–301. [Google Scholar]
  4. F. Cribari-Neto, K.L.P. Vasconcellos and N.L. Garcia, A note on inverse moments of binomial variates. Br. Rev. Econometr. 20 (2000) 269–277. [Google Scholar]
  5. S. Dabo-Niang, Kernel density estimator in an infinite-dimensional space with a rate of convergence in the case of diffusion process. Appl. Math. Lett. 17 (2004) 381–386. [Google Scholar]
  6. S. Dabo-Niang, F. Ferraty and P. Vieu, On the using of modal curves for radar waveforms classification. Comput. Stat. Data Anal. 51 (2007) 4878–4890. [Google Scholar]
  7. A. Delaigle and P. Hall, Defining probability density for a distribution of random functions. Ann. Stat. 38 (2010) 1171–1193. [Google Scholar]
  8. A. Dutt and V. Rokhlin, Fast Fourier transforms for nonequispaced data. SIAM J. Sci. Comput. 14 (1993) 1368–1393. [Google Scholar]
  9. F. Ferraty and P. Vieu, Curves discrimination: a nonparametric functional approach. Comput. Stat. Data Anal. 44 (2003) 161–173. [Google Scholar]
  10. R. Fraiman, J. Meloche, L.A. García-Escudero, A. Gordaliza, X. He, R. Maronna, V.J. Yohai, S.J. Sheather, J.W. McKean and C.G. Small et al., Multivariate L-estimation. Test 8 (1999) 255–317. [CrossRef] [MathSciNet] [Google Scholar]
  11. I.K. Glad, N.L. Hjort and N.G. Ushakov, Correction of density estimators that are not densities. Scand. J. Stat. 30 (2003) 415–427. [Google Scholar]
  12. L. Greengard and J.-Y. Lee, Accelerating the nonuniform fast Fourier transform. SIAM Rev. 46 (2004) 443–454. [Google Scholar]
  13. B. Gregorutti, B. Michel and P. Saint-Pierre, Grouped variable importance with random forests and application to multiple functional data analysis. Comput. Stat. Data Anal. 90 (2015) 15–35. [Google Scholar]
  14. P. Hall and N.E. Heckman, Estimating and depicting the structure of a distribution of random functions. Biometrika 89 (2002) 145–158. [Google Scholar]
  15. J. Jacod, Lecture notes on “Mouvement brownien et calcul stochastique” (2007). [Google Scholar]
  16. T. Kanamori, S. Hido and M. Sugiyama, A least-squares approach to direct importance estimation. J. Machine Learning Research 10 (2009) 1391–1445. [Google Scholar]
  17. S. López-Pintado and J. Romo, On the concept of depth for functional data. J. Amer. Stat. Assoc. 104 (2009) 718–734. [Google Scholar]
  18. K. Makiyama, densratio, A Python Package for Density Ratio Estimation, Dec. 2016, Dowloaded on may 4th (2018). [Google Scholar]
  19. F. Nicol, Functional principal component analysis of aircraft trajectories, In Proceedings of the 2nd International Conference on Interdisciplinary Science for Innovative Air Traffic Management (ISIATM) (2013). [Google Scholar]
  20. A. Nieto-Reyes and H. Battey, A topologically valid definition of depth for functional data. Statistical Science 31 (2016) 61–79. [Google Scholar]
  21. T.A. O’Brien, W.D. Collins, S.A. Rauscher and T.D. Ringler, Reducing the Computational Cost of the ECF Using a nuFFT: A Fast and Objective Probability Density Estimation Method. Comp. Stat. Data Anal. 79 (2014) 222–234. [Google Scholar]
  22. T.A. O’Brien, K. Kashinath, N.R Cavanaugh, W.D. Collins and J.P. O’Brien, A fast and objective multidimensional kernel density estimation method: fastkde. Comp. Stat. Data Anal. 101 (2016) 148–160. [Google Scholar]
  23. F. Pedregosa et al., Scikit-learn: Machine learning in Python. JMLR 12 (2011) 2825–2830. [Google Scholar]
  24. B.L.S. Prakasa Rao Nonparametric density estimation for functional data by delta sequences. Braz. J. Probab. Stat. 24 (2010) 468–478. [Google Scholar]
  25. B.L.S. Prakasa Rao Nonparametric density estimation for functional data via wavelets. Commun. Stat. Theory Meth. 39 (2010) 1608–1618. [Google Scholar]
  26. J.O. Ramsay and B.W. Silverman. Applied functional data analysis: methods and case studies. Springer (2007). [Google Scholar]
  27. C. Rommel, J.F. Bonnans, B Gregorutti and P. Martinon, Aircraft dynamics identification for optimal control, in Proceedings of the 7th European Conference for Aeronautics and Aerospace Sciences (2017). [Google Scholar]
  28. C. Rommel, J.F. Bonnans, P. Martinon and B. Gregorutti, Gaussian Mixture Penalty for Trajectory Optimization Problems. J. Guidance, Control Dyn. 42 (2019) 1857–1863. [Google Scholar]
  29. D.W. Scott. Multivariate density estimation: theory, practice, and visualization. John Wiley & Sons (2015). [Google Scholar]
  30. B.W. Silverman. Density Estimation for Statistics and Data Analysis. Chapman & Hall (1986). [Google Scholar]
  31. C.J. Stone, An asymptotically optimal window selection rule for kernel density estimates. Ann. Stat. 12 (1984) 1285–1297. [Google Scholar]
  32. M. Sugiyama, I. Takeuchi, T. Suzuki, T. Kanamori, H. Hachiya and D. Okanohara, Conditional density estimation via least-squares density ratio estimation, In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (2010) 781–788. [Google Scholar]
  33. A.B. Tsybakov. Introduction to Nonparametric Estimation. Springer, 1st edition (2008). [Google Scholar]
  34. L. Wasserman. All of statistics: a concise course in statistical Inference. Springer texts in statistics. Springer (2004). [Google Scholar]
  35. G.S. Watson and M.R. Leadbetter, On the estimation of the probability density. Ann. Math. Stat. 34 (1963) 480–491. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.