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 |
Show full item record
Files in This Item:
There are no files associated with this item.
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.