https://hal-enac.archives-ouvertes.fr/hal-01799104Nicol, FlorenceFlorenceNicolENAC - Ecole Nationale de l'Aviation CivileStatistical Analysis of Aircraft Trajectories: a Functional Data Analysis ApproachHAL CCSD2017curve clusteringprincipal component analysisfunctional statisticsair traffic management[MATH.MATH-ST] Mathematics [math]/Statistics [math.ST][STAT.AP] Statistics [stat]/Applications [stat.AP][STAT.ME] Statistics [stat]/Methodology [stat.ME]Nicol, Florence2018-05-24 13:11:222021-11-03 05:14:392018-05-31 11:03:58enConference papersapplication/pdf1In Functional Data Analysis, the underlying structure of a raw observation is functional and data are assumed to be sample paths from a single stochastic process. When data considered are functional in nature thus infinite-dimensional, like curves or images, the multivariate statistical procedures have to be generalized to the infinite-dimensional case. By approximating random functions by a finite number of random score vectors, the Principal Component Analysis approach appears as a dimension reduction technique and offers a visual tool to assess the dominant modes of variation, pattern of interest, clusters in the data and outlier detection. A functional statistics approach is applied to univariate and multivariate aircraft trajectories.