IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v24y2015i2p307-312.html
   My bibliography  Save this article

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

Author

Listed:
  • Anestis Antoniadis
  • Jean-Michel Poggi

Abstract

No abstract is available for this item.

Suggested Citation

  • Anestis Antoniadis & Jean-Michel Poggi, 2015. "Discussion of “Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 307-312, July.
  • Handle: RePEc:spr:stmapp:v:24:y:2015:i:2:p:307-312
    DOI: 10.1007/s10260-015-0309-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10260-015-0309-8
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10260-015-0309-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Boente, Graciela & Rodriguez, Daniela & Sued, Mariela, 2010. "Inference under functional proportional and common principal component models," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 464-475, February.
    2. Yehua Li & Yongtao Guan, 2014. "Functional Principal Component Analysis of Spatiotemporal Point Processes With Applications in Disease Surveillance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1205-1215, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Piercesare Secchi & Simone Vantini & Valeria Vitelli, 2015. "Rejoinder to the discussion of “Analysis of Spatio-Temporal Mobile Phone Data: a Case Study in the Metropolitan Area of Milan”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 335-338, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giraldo, Ramón & Dabo-Niang, Sophie & Martínez, Sergio, 2018. "Statistical modeling of spatial big data: An approach from a functional data analysis perspective," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 126-129.
    2. Marc Hallin & Davy Paindaveine & Thomas Verdebout, 2014. "Efficient R-Estimation of Principal and Common Principal Components," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1071-1083, September.
    3. Zheng, Xueying & Xue, Lan & Qu, Annie, 2018. "Time-varying correlation structure estimation and local-feature detection for spatio-temporal data," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 221-239.
    4. Han Shang, 2014. "A survey of functional principal component analysis," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(2), pages 121-142, April.
    5. Zhang, Xin & Wang, Chong & Wu, Yichao, 2018. "Functional envelope for model-free sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 163(C), pages 37-50.
    6. Jiří Dvořák & Michaela Prokešová, 2016. "Parameter Estimation for Inhomogeneous Space-Time Shot-Noise Cox Point Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 939-961, December.
    7. Graciela Boente & Daniela Rodriguez & Mariela Sued, 2018. "Testing equality between several populations covariance operators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 919-950, August.
    8. Li, Yehua & Qiu, Yumou & Xu, Yuhang, 2022. "From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    9. Haozhe Zhang & Yehua Li, 2020. "Unified Principal Component Analysis for Sparse and Dense Functional Data under Spatial Dependency," Papers 2006.13489, arXiv.org, revised Jun 2021.
    10. Song, Jun & Li, Bing, 2021. "Nonlinear and additive principal component analysis for functional data," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
    11. Bali, Juan Lucas & Boente, Graciela, 2017. "Robust estimators under a functional common principal components model," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 424-440.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stmapp:v:24:y:2015:i:2:p:307-312. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.