Topological regularization with information filtering networks
نویسندگان
چکیده
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 (IFN). can be directly applied covariance selection problem providing an instrument for sparse with both linear non-linear probability distributions such as elliptical generalized hyperbolic families. It also implemented of multicollinear regression. In this paper, I describe detail application Student-t. A specific expectation–maximization likelihood maximization procedure over chordal network representation is proposed Student-t case. Examples real data from stock prices log-returns artificially generated demonstrate applicability, performances, robustness potentials methodology.
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ژورنال
عنوان ژورنال: Information Sciences
سال: 2022
ISSN: ['0020-0255', '1872-6291']
DOI: https://doi.org/10.1016/j.ins.2022.06.007