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G-Lasso Network Analysis for Functional Data

  • Lara Fontanella
  • Sara FontanellaEmail author
  • Rosaria Ignaccolo
  • Luigi Ippoliti
  • Pasquale Valentini
Conference paper
  • 138 Downloads
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

Network analytical tools are becoming increasingly popular in analysing interdependent and interacting data entities. Statistical modelling of network data seeks to recover the underlying relational structure of the data capturing relevant characteristics and regularities in the pattern of interactions. This framework is widely adopted in multivariate data setting. However, in many applications, data are naturally regarded as random functions rather than multivariate vectors. In this work, we propose a simple approach to extend network analytical tools to the functional data setting. Specifically, we show that the graph representation of a set of functions can be retrieved through the precision matrix of a Gaussian Process, which encodes the conditional dependence structure among functional data. By using the standard graphical Lasso algorithm, preliminary results of the proposed methodology are shown for a benchmark dataset of daily average temperatures.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Lara Fontanella
    • 1
  • Sara Fontanella
    • 2
    Email author
  • Rosaria Ignaccolo
    • 3
  • Luigi Ippoliti
    • 1
  • Pasquale Valentini
    • 1
  1. 1.University of Chieti-PescaraPescaraItaly
  2. 2.University of Torino, Italy and Imperial College LondonTorinoItaly
  3. 3.University of TorinoTorinoItaly

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