Repositorio de producción científica de la Universidad de Sevilla

A sparsity-controlled vector autoregressive model

Opened Access A sparsity-controlled vector autoregressive model

Citas

buscar en

Estadísticas
Icon
Exportar a
Autor: Carrizosa Priego, Emilio José
Olivares Nadal, Alba Victoria
Ramírez Cobo, Josefa
Departamento: Universidad de Sevilla. Departamento de Estadística e Investigación Operativa
Fecha: 2017-04
Publicado en: Biostatistics, 18 (2), 244-259.
Tipo de documento: Artículo
Resumen: Vector autoregressive (VAR) models constitute a powerful and well studied tool to analyze multivariate time series. Since sparseness, crucial to identify and visualize joint dependencies and relevant causalities, is not expected to happen in the standard VAR model, several sparse variants have been introduced in the literature. However, in some cases it might be of interest to control some dimensions of the sparsity, as e.g. the number of causal features allowed in the prediction. To authors extent none of the existent methods endows the user with full control over the different aspects of the sparsity of the solution. In this paper we propose a sparsity-controlled VAR model which allows to control different dimensions of the sparsity, enabling a proper visualization of potential causalities and dependencies. The model coefficients are found as the solution to a mathematical optimization problem, solvable by standard numerical optimization routines. The tests performed on bot...
[Ver más]
Tamaño: 3.466Mb
Formato: PDF

URI: http://hdl.handle.net/11441/68116

DOI: 10.1093/biostatistics/kxw042

Ver versión del editor

Mostrar el registro completo del ítem


Esta obra está bajo una Licencia Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internacional

Este registro aparece en las siguientes colecciones