Discussion of the paper “analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan”

Orietta Nicolis () and Jorge Mateu ()

Statistical Methods & Applications, 2015, vol. 24, issue 2, 315-319

Abstract: 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. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Basis functions; Dimension reduction; Gaussian random fields; Spatially dependent functional data; Spatio-temporal stochastic models (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1007/s10260-015-0311-1 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:24:y:2015:i:2:p:315-319

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-015-0311-1

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().