BRIDGE ESTIMATION FOR LINEAR REGRESSION MODELS WITH MIXING PROPERTIES

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Penalized regression methods have for quite some time been a popular choice for addressing challenges in high dimensional data analysis. Despite their popularity, their application to time series data has been limited. This paper concerns bridge penalized methods in a linear regression time series model. We first prove consistency, sparsity and asymptotic normality of bridge estimators under a general mixing model. Next, as a special case of mixing errors, we consider bridge regression with autoregressive and moving average (ARMA) error models and develop a computational algorithm that can simultaneously select important predictors and the orders of ARMA models. Simulated and real data examples demonstrate the effective performance of the proposed algorithm and the improvement over ordinary bridge regression.
Publisher
WILEY
Issue Date
2014-09
Language
English
Article Type
Article
Citation

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, v.56, no.3, pp.283 - 302

ISSN
1369-1473
DOI
10.1111/anzs.12075
URI
http://hdl.handle.net/10203/285757
Appears in Collection
MA-Journal Papers(저널논문)
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