Name
Abstract | ||
|---|---|---|
This paper introduces a novel methodology to perform topological regularization in multivariate probabilistic modeling by using sparse, complex, networks which represent the system’s dependency structure and are called information filtering networks (IFN). This methodology can be directly applied to covariance selection problem providing an instrument for sparse probabilistic modeling with both linear and non-linear multivariate probability distributions such as the elliptical and generalized hyperbolic families. It can also be directly implemented for topological regularization of multicollinear regression. In this paper, I describe in detail an application to sparse modeling with multivariate Student-t. A specific expectation–maximization likelihood maximization procedure over a sparse chordal network representation is proposed for this sparse Student-t case. Examples with real data from stock prices log-returns and from artificially generated data demonstrate applicability, performances, robustness and potentials of this methodology. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ins.2022.06.007 | Information Sciences |
Keywords | DocType | Volume |
Topological regularization,Information filtering networks,Complex systems,Covariance selection,Sparse inverse covariance,Chow-Liu Trees,Sparse expectation-maximization,IFN regression | Journal | 608 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
0 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomaso Aste | 1 | 57 | 11.62 |