Bayes shrinkage estimation for high-dimensional VAR models with scale mixture of normal distributions for noise

Cited 2 time in webofscience Cited 0 time in scopus
  • Hit : 218
  • Download : 0
We propose Bayesian shrinkage methods for coefficient estimation for high-dimensional vector autoregressive (VAR) models using scale mixtures of multivariate normal distributions for independently sampled additive noises. We also suggest an efficient selection procedure for the shrinkage parameter as a computationally feasible alternative to the traditional MCMC sampling methods for high-dimensional data. A shrinkage parameter is selected at the minimum point of a newly proposed score function which is asymptotically equivalent to the mean squared error of the model coefficients. The selected shrinkage parameter is presented in a closed form as a function of sample size, level of noise, and non-normality in data, and it can be efficiently estimated by using a suggested variation of cross validation. Consistency of both of the cross validation estimator and proposed shrinkage estimator is proved. The competitiveness of the proposed methods is demonstrated based on comprehensive experimental results using simulated data and high-dimensional plant gene expression data in the context of coefficient estimation and structural inference for VAR models. The proposed methods are applicable to high dimensional stationary time series with or without near unit roots. (C) 2016 Elsevier B.V. All rights reserved
Publisher
ELSEVIER SCIENCE BV
Issue Date
2016-09
Language
English
Article Type
Article
Keywords

CROSS-VALIDATION; TIME-SERIES; REGRESSION; SELECTION; CONSISTENCY; LIKELIHOOD

Citation

COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.101, pp.250 - 276

ISSN
0167-9473
DOI
10.1016/j.csda.2016.03.007
URI
http://hdl.handle.net/10203/212092
Appears in Collection
MA-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 2 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0