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Sparsity-Promoting Bayesian Dynamic Linear Models

François Caron 1, 2 Luke Bornn 3 Arnaud Doucet 4 
1 ALEA - Advanced Learning Evolutionary Algorithms
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5251
Abstract : Sparsity-promoting priors have become increasingly popular over recent years due to an increased number of regression and classification applications involving a large number of predictors. In time series applications where observations are collected over time, it is often unrealistic to assume that the underlying sparsity pattern is fixed. We propose here an original class of flexible Bayesian linear models for dynamic sparsity modelling. The proposed class of models expands upon the existing Bayesian literature on sparse regression using generalized multivariate hyperbolic distributions. The properties of the models are explored through both analytic results and simulation studies. We demonstrate the model on a financial application where it is shown that it accurately represents the patterns seen in the analysis of stock and derivative data, and is able to detect major events by filtering an artificial portfolio of assets.
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Submitted on : Wednesday, February 29, 2012 - 4:03:27 PM
Last modification on : Friday, February 4, 2022 - 3:23:56 AM
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  • HAL Id : hal-00675274, version 1
  • ARXIV : 1203.0106



François Caron, Luke Bornn, Arnaud Doucet. Sparsity-Promoting Bayesian Dynamic Linear Models. [Research Report] RR-7895, INRIA. 2012, pp.23. ⟨hal-00675274⟩



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