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


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....


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.


  1. Aldor-Noiman S, Feigin P, Mandelbaum A (2009) Workload forecasting for a call center: methodology and a case study. Ann Appl Stat 3:1403–1447MathSciNetCrossRefzbMATHGoogle Scholar
  2. Antoniadis A, Mohamed H, Glad I (2014) Nonparametric local comparison of empirical distributions via nonparametric regression. J Stat Comput Simul. doi: 10.1080/00949655.2014.929133
  3. Benko M, Haerdle W, Kneip A (2009) Common functional principal components. Ann Stat 37(1):1–34CrossRefzbMATHGoogle Scholar
  4. Boente G, Rodriguez D, Sued M (2010) Inference under functional proportional and common principal component models. J Multivar Anal 101:464–475MathSciNetCrossRefzbMATHGoogle Scholar
  5. Brown LD, Cai T, Zhang R, Zhao L, Zhou H (2010) The root–unroot algorithm for density estimation as implemented via wavelet block thresholding. Probab Theory Relat Fields 146:401433MathSciNetCrossRefGoogle Scholar
  6. Chang Y-M, Hsu N-J, Huang H-C (2010) Semiparametric estimation and selection for nonstationary spatial covariance functions. J Comput Graph Stat 19(1):117–139MathSciNetCrossRefzbMATHGoogle Scholar
  7. Coifman R, Saito N (1996) The local Karhunen-Loève bases. In: Proceedings of IEEE international symposium on time-frequency and time-scale analysis. IEEE Signal Processing Society, 129–132Google Scholar
  8. Fink D, Hochachka W, Zuckerberg B, Winkler D, Shaby B, Munson M, Hooker G, Riedewald M, Sheldon D, Kelling S (2010) Spatiotemporal exploratory models for broad-scale survey data. Ecol Appl 20(8):2131–2147CrossRefGoogle Scholar
  9. Flury BK (1984) Common principal components in k groups. J Am Statist Assoc 79:892–898MathSciNetGoogle Scholar
  10. Ibrahim R, L’Ecuyer P, Regnard N, Shen H (2012) On modelling and forecasting of call center arrivals. In: Laroque C, Himmelspach J, Pasupathy R, Rose O, Uhrmacher AM (eds) 2012 winter simulation conference proceedingsGoogle Scholar
  11. Lee AB, Nadler B, Wasserman L (2008) Treelets—An adaptive multi-scale basis for sparse unordered data. Ann Appl Stat 2(2):435–471MathSciNetCrossRefzbMATHGoogle Scholar
  12. Lee AB, Nadler B (2007) Treelets—A tool for dimensionality reduction and multi-scale analysis of unstructured data. In: Meila M, Shen Y (eds) Proceedings of the eleventh international conference on artificial intelligence and statisticsGoogle Scholar
  13. Li Y, Guan Y (2014) Functional principal component analysis of spatiotemporal point processes with applications in disease surveillance. J Am Stat Assoc 109:507:1205–1215. doi: 10.1080/01621459.2014.885434 MathSciNetCrossRefGoogle Scholar
  14. Lindström J, Szpiro A, Sampson PLD, Bergen S, Oron AP (2013) Spatiotemporal (version 1.1.7): an R package for Spatio-temporal model estimation.

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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