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Statistical Methods & Applications

, Volume 24, Issue 2, pp 307–312 | Cite as

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

  • Anestis Antoniadis
  • Jean-Michel PoggiEmail author
Discussion

Introduction

We congratulate the authors for a very interesting and timely contribution to an important problem: using nonparametric functional data analysis methods for studying spatio-temporal observations with the aim to identify subregions in a spatial domain sharing similar patterns along time.

The authors develop an efficient methodology to perform dimensional reduction of spatially dependent functional data based on treelet decompositions and an appropriate bagging Voronoi strategy that allows them to take into account the spatial dependency in the analysed data. Treelets were first introduced in Lee and Nadler (2007) and Lee et al. (2008) as a multi-scale basis that extends wavelets to unordered data. The method is fully adaptive. It returns orthonormal basis functions supported on nested clusters in a hierarchical tree. Unlike other hierarchical methods, the basis and the tree structure are computed simultaneously, and both reflect the internal structure of the data....

Keywords

Implied Volatility Functional Principal Component Mobile Phone Data Time Dependent Rate Local Basis Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Laboratoire Jean KuntzmannUniversity Joseph FourierGrenobleFrance
  2. 2.Department of Statistical SciencesUniversity of Cape TownCape TownSouth Africa
  3. 3.Laboratoire de mathématiques Orsay (LMO)University Paris-SudOrsayFrance
  4. 4.University Paris DescartesParisFrance

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