Penalized regression models with autoregressive error terms

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Penalized regression methods have recently gained enormous attention in statistics and the field of machine learning due to their ability of reducing the prediction error and identifying important variables at the same time. Numerous studies have been conducted for penalized regression, but most of them are limited to the case when the data are independently observed. In this paper, we study a variable selection problem in penalized regression models with autoregressive (AR) error terms. We consider three estimators, adaptive least absolute shrinkage and selection operator, bridge, and smoothly clipped absolute deviation, and propose a computational algorithm that enables us to select a relevant set of variables and also the order of AR error terms simultaneously. In addition, we provide their asymptotic properties such as consistency, selection consistency, and asymptotic normality. The performances of the three estimators are compared with one another using simulated and real examples.
Publisher
TAYLOR & FRANCIS LTD
Issue Date
2013-09
Language
English
Article Type
Article
Citation

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.83, no.9, pp.1756 - 1772

ISSN
0094-9655
DOI
10.1080/00949655.2012.669383
URI
http://hdl.handle.net/10203/285776
Appears in Collection
MA-Journal Papers(저널논문)
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