Quantifying the closeness to a set of random curves via the mean marginal likelihood
ESAIM: Probability and Statistics, Tome 25 (2021), pp. 1-30

In this paper, we tackle the problem of quantifying the closeness of a newly observed curve to a given sample of random functions, supposed to have been sampled from the same distribution. We define a probabilistic criterion for such a purpose, based on the marginal density functions of an underlying random process. For practical applications, a class of estimators based on the aggregation of multivariate density estimators is introduced and proved to be consistent. We illustrate the effectiveness of our estimators, as well as the practical usefulness of the proposed criterion, by applying our method to a dataset of real aircraft trajectories.

DOI : 10.1051/ps/2020028
Classification : 62G07, 62G05, 62P30, 62M09
Keywords: Density estimation, functional data analysis, trajectory discrimination, kernel density estimator
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     author = {Rommel, C\'edric and Fr\'ed\'eric Bonnans, J. and Gregorutti, Baptiste and Martinon, Pierre},
     title = {Quantifying the closeness to a set of random curves \protect\emph{ via} the mean marginal likelihood},
     journal = {ESAIM: Probability and Statistics},
     pages = {1--30},
     year = {2021},
     publisher = {EDP-Sciences},
     volume = {25},
     doi = {10.1051/ps/2020028},
     mrnumber = {4224732},
     zbl = {1466.62288},
     language = {en},
     url = {https://www.numdam.org/articles/10.1051/ps/2020028/}
}
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Rommel, Cédric; Frédéric Bonnans, J.; Gregorutti, Baptiste; Martinon, Pierre. Quantifying the closeness to a set of random curves  via the mean marginal likelihood. ESAIM: Probability and Statistics, Tome 25 (2021), pp. 1-30. doi: 10.1051/ps/2020028

[1] A. Bernacchia and S. Pigolotti, Self-consistent method for density estimation. J. Royal Stat. Soc. 73 (2011) 407–422. | MR | Zbl | DOI

[2] D. Bosq, Nonparametric statistics for stochastic processes: estimation and prediction. In Vol. 10. Springer Science & Business Media (2012).

[3] J.W. Cooley and J.W. Tukey, An algorithm for the machine calculation of complex Fourier series. Math. Comput. 19 (1965) 297–301. | MR | Zbl | DOI

[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.

[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. | MR | Zbl | DOI

[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. | MR | Zbl | DOI

[7] A. Delaigle and P. Hall, Defining probability density for a distribution of random functions. Ann. Stat. 38 (2010) 1171–1193. | MR | Zbl | DOI

[8] A. Dutt and V. Rokhlin, Fast Fourier transforms for nonequispaced data. SIAM J. Sci. Comput. 14 (1993) 1368–1393. | MR | Zbl | DOI

[9] F. Ferraty and P. Vieu, Curves discrimination: a nonparametric functional approach. Comput. Stat. Data Anal. 44 (2003) 161–173. | MR | Zbl | DOI

[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. | Zbl | DOI

[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. | MR | Zbl | DOI

[12] L. Greengard and J.-Y. Lee, Accelerating the nonuniform fast Fourier transform. SIAM Rev. 46 (2004) 443–454. | MR | Zbl | DOI

[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. | MR | Zbl | DOI

[14] P. Hall and N.E. Heckman, Estimating and depicting the structure of a distribution of random functions. Biometrika 89 (2002) 145–158. | MR | Zbl | DOI

[15] J. Jacod, Lecture notes on “Mouvement brownien et calcul stochastique” (2007).

[16] T. Kanamori, S. Hido and M. Sugiyama, A least-squares approach to direct importance estimation. J. Machine Learning Research 10 (2009) 1391–1445. | MR | Zbl

[17] S. López-Pintado and J. Romo, On the concept of depth for functional data. J. Amer. Stat. Assoc. 104 (2009) 718–734. | MR | Zbl | DOI

[18] K. Makiyama, densratio, A Python Package for Density Ratio Estimation, Dec. 2016, Dowloaded on may 4th (2018).

[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).

[20] A. Nieto-Reyes and H. Battey, A topologically valid definition of depth for functional data. Statistical Science 31 (2016) 61–79. | MR | Zbl | DOI

[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. | MR | Zbl | DOI

[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. | MR | Zbl | DOI

[23] F. Pedregosa et al., Scikit-learn: Machine learning in Python. JMLR 12 (2011) 2825–2830. | MR | Zbl

[24] B.L.S. Prakasa Rao Nonparametric density estimation for functional data by delta sequences. Braz. J. Probab. Stat. 24 (2010) 468–478. | MR | Zbl

[25] B.L.S. Prakasa Rao Nonparametric density estimation for functional data via wavelets. Commun. Stat. Theory Meth. 39 (2010) 1608–1618. | Zbl | DOI

[26] J.O. Ramsay and B.W. Silverman. Applied functional data analysis: methods and case studies. Springer (2007). | Zbl

[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).

[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. | DOI

[29] D.W. Scott. Multivariate density estimation: theory, practice, and visualization. John Wiley & Sons (2015). | MR | Zbl | DOI

[30] B.W. Silverman. Density Estimation for Statistics and Data Analysis. Chapman & Hall (1986). | MR | Zbl

[31] C.J. Stone, An asymptotically optimal window selection rule for kernel density estimates. Ann. Stat. 12 (1984) 1285–1297. | MR | Zbl | DOI

[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.

[33] A.B. Tsybakov. Introduction to Nonparametric Estimation. Springer, 1st edition (2008). | MR | Zbl

[34] L. Wasserman. All of statistics: a concise course in statistical Inference. Springer texts in statistics. Springer (2004). | MR | Zbl

[35] G.S. Watson and M.R. Leadbetter, On the estimation of the probability density. Ann. Math. Stat. 34 (1963) 480–491. | MR | Zbl | DOI

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