caa a22 4500 606196676 CHVBK 20210128100925.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s10260-015-0311-1 doi (NATIONALLICENCE)springer-10.1007/s10260-015-0311-1 Discussion of the paper "analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan” [Elektronische Daten] [Orietta Nicolis, Jorge Mateu] The authors are to be congratulated on a valuable and thought-provoking contribution on the analysis of geo-referenced high-dimensional data describing the use over time of the mobile-phone network in the urban area of Milan, Italy. This is a timely and world-wide problem that opens wide avenues for new methodological contributions. The authors develop a Bagging Voronoi Treelet Analysis which is a non-parametric method for the analysis of spatially dependent functional data. This approach integrates the treelet decomposition with a proper treatment of spatial dependence, obtained through a Bagging Voronoi strategy. In our discussion, we focus on the following points: (i) a mobre general form of the spatio-temporal model proposed in Secchi et al. (Stat Methods Appl, 2015), (ii) alternative methods to approach the smooth temporal functions, (iii) additional methods to reduce the problem of dimension for spatial dependence data, and (iv) comments on the pros and cons of the proposed pre-processing methodology. Springer-Verlag Berlin Heidelberg, 2015 Basis functions nationallicence Dimension reduction nationallicence Gaussian random fields nationallicence Spatially dependent functional data nationallicence Spatio-temporal stochastic models nationallicence Nicolis Orietta Institute of Statistics, University of Valparaiso, Valparaiso, Chile aut Mateu Jorge Department of Mathematics, University Jaume I of Castellón, Castellón, Spain aut Statistical Methods & Applications Springer Berlin Heidelberg 24/2(2015-07-01), 315-319 1618-2510 24:2<315 2015 24 10260 https://doi.org/10.1007/s10260-015-0311-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10260-015-0311-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Nicolis Orietta Institute of Statistics, University of Valparaiso, Valparaiso, Chile aut NATIONALLICENCE 700 1- Mateu Jorge Department of Mathematics, University Jaume I of Castellón, Castellón, Spain aut NATIONALLICENCE 773 0- Statistical Methods & Applications Springer Berlin Heidelberg 24/2(2015-07-01), 315-319 1618-2510 24:2<315 2015 24 10260