Robust Time-Varying Undirected Graphs

Mauro Bernardi () and Paola Stolfi ()
Additional contact information
Mauro Bernardi: University of Padova, Department of Statistical Sciences
Paola Stolfi: Roma Tre University and Istituto per le Applicazioni del Calcolo “Mauro Picone” - CNR, Department of Economics

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2018, pp 117-120 from Springer

Abstract: Abstract Undirected graphs are useful tools for the analysis of sparse and high-dimensional data sets. In this setting the sparsity helps in reducing the complexity of the model. However, sparse graphs are usually estimated under the Gaussian paradigm thereby leading to estimates that are very sensitive to the presence of outlying observations. In this paper we deal with sparse time-varying undirected graphs, namely sparse graphs whose structure evolves over time. Our contribution is to provide a robustification of these models, in particular we propose a robust estimator which minimises the γ-divergence. We provide an algorithm for the parameter estimation and we investigate the rate of convergence of the proposed estimator.

Keywords: Divergence; Kernel methods; Robust methods; Dynamic models; Graphical models (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-89824-7_21

Ordering information: This item can be ordered from
http://www.springer.com/9783319898247

DOI: 10.1007/978-3-319-89824-7_21

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().