Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/105434
Title: Threshold variable selection via a L 1 penalty approach
Authors: Jiang, Q.
Xia, Y. 
Keywords: Adaptive lasso
Oracle property
Smooth threshold AR model
Variable selection
Issue Date: 2011
Citation: Jiang, Q.,Xia, Y. (2011). Threshold variable selection via a L 1 penalty approach. Statistics and its Interface 4 (2) : 137-148. ScholarBank@NUS Repository.
Abstract: Selecting the threshold variable is a key step in building a general threshold autoregressive (TAR) model. Based on a general smooth threshold autoregressive (STAR) model, we propose to select the threshold variable by the recently developed L 1-penalizing approach. Moreover, by penalizing the direction of the coefficient vector instead of the coefficients themselves, the threshold variable is more accurately selected. Oracle properties of the estimator are obtained. Its advantage is shown with both numerical and real data analysis.
Source Title: Statistics and its Interface
URI: http://scholarbank.nus.edu.sg/handle/10635/105434
ISSN: 19387989
Appears in Collections:Staff Publications

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