TY - JOUR
T1 - Discussion of the paper “analysis of spatio-temporal mobile phone data
T2 - a case study in the metropolitan area of Milan”
AU - Nicolis, Orietta
AU - Mateu, Jorge
N1 - Funding Information:
The present work has been partially supported by Fondecyt grant 1131147 from the Chilean government, and research funds of the University of Palermo with reference 2012-ATE-0332. Additionally, partial funding comes from grants P1-1B2012-52, and MTM2013-43917-P from Ministery of Economy and Competitivity.
Publisher Copyright:
© 2015, Springer-Verlag Berlin Heidelberg.
PY - 2015/7/7
Y1 - 2015/7/7
N2 - 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.
AB - 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.
KW - Basis functions
KW - Dimension reduction
KW - Gaussian random fields
KW - Spatially dependent functional data
KW - Spatio-temporal stochastic models
UR - http://www.scopus.com/inward/record.url?scp=84938742949&partnerID=8YFLogxK
U2 - 10.1007/s10260-015-0311-1
DO - 10.1007/s10260-015-0311-1
M3 - Article
AN - SCOPUS:84938742949
SN - 1618-2510
VL - 24
SP - 315
EP - 319
JO - Statistical Methods and Applications
JF - Statistical Methods and Applications
IS - 2
ER -