Nested covariance functions on graphs with Euclidean edges cross time

Emilio Porcu, Xavier Emery, Ana Paula Peron

Research output: Contribution to journalArticlepeer-review

Abstract

Covariance functions over generalized networks have been explored to a very limited extent. We consider nested spatial or space-time covariance models, where space is a generalized network, and where time can be linear (the real line) or circular. We show sufficient conditions allowing preservation of positive semidefiniteness when at least one of the weights involved in the linear combination is negative. Several examples illustrate our findings. In particular, we show nested constructions for Euclidean trees with a finite number of leaves involving basic covariance functions with different scale parameters or different compact supports. We also provide criteria that allow one to build space-time models through half spectral modeling on graphs cross linear or circular time.

Original languageBritish English
Pages (from-to)4222-4246
Number of pages25
JournalElectronic Journal of Statistics
Volume16
Issue number2
DOIs
StatePublished - 2022

Keywords

  • circular time
  • Covariance functions
  • Euclidean trees
  • generalized networks
  • graphs with Euclidean edges
  • linear time

Fingerprint

Dive into the research topics of 'Nested covariance functions on graphs with Euclidean edges cross time'. Together they form a unique fingerprint.

Cite this