التفاصيل البيبلوغرافية
| العنوان: |
Adaptive L1 regularized second-order least squares method for model selection |
| المؤلفون: |
Xue, Lin |
| المساهمون: |
Fu, James (Statistics) Torabi, Mahmoud (Community Health Sciences), Wang, Liqun (Statistics) Jiang, Depeng (Community Health Sciences) |
| سنة النشر: |
2015 |
| المجموعة: |
MSpace at the University of Manitoba |
| مصطلحات موضوعية: |
Adaptive LASSO, Second-order least squares method, Variable selection |
| الوصف: |
The second-order least squares (SLS) method in regression model proposed by Wang (2003, 2004) is based on the first two conditional moments of the response variable given the observed predictor variables. Wang and Leblanc (2008) show that the SLS estimator (SLSE) is asymptotically more efficient than the ordinary least squares estimator (OLSE) if the third moment of the random error is nonzero. We apply the SLS method to variable selection problems and propose the adaptively weighted L1 regularized SLSE (L1-SLSE). The L1-SLSE is robust against the shape of error distributions in variable selection problems. Finite sample simulation studies show that the L1-SLSE is more efficient than L1-OLSE in the case of asymmetric error distributions. A real data application with L1-SLSE is presented to demonstrate the usage of this method. ; October 2015 |
| نوع الوثيقة: |
master thesis |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| العلاقة: |
http://hdl.handle.net/1993/30757Test |
| الإتاحة: |
http://hdl.handle.net/1993/30757Test |
| حقوق: |
open access |
| رقم الانضمام: |
edsbas.92A75F92 |
| قاعدة البيانات: |
BASE |
