CARON, François; BORNN, Luke; DOUCET, Arnaud

Metadatos

Mostrar el registro completo del ítem

Licencia de uso del documento

CARON, François
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]

BORNN, Luke
Department of Statistics [Vancouver] [UBC Statistics]

DOUCET, Arnaud
Department of Statistics [Oxford]

Idioma

Rapport

Este ítem está publicado en

2012-02p. 23

Resumen en inglés

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.< Leer menos

Palabras clave en inglés

generalized hyperbolic

Gaussian mixture models

sparsity

dynamic regression

particle filters

particle MCMC

Orígen

Importado de Hal

Centros de investigación

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251