Variable Selection Incorporating Prior Constraint Information into Lasso
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Variable Selection Incorporating Prior Constraint Information into Lasso
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| Id. |
25663075 |
| Titulo |
Variable Selection Incorporating Prior Constraint Information into Lasso |
| Autor(es) |
Song, Shurong Zheng; Guodong Shi, Ning-Zhong |
| Localización |
http://arxiv.org/abs/0705.4588
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| Versión |
1.0 |
| Estado |
Final
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| Descripción |
We propose the variable selection procedure incorporating prior constraint
information into lasso. The proposed procedure combines the sample and prior
information, and selects significant variables for responses in a narrower
region where the true parameters lie. It increases the efficiency to choose the
true model correctly. The proposed procedure can be executed by many
constrained quadratic programming methods and the initial estimator can be
found by least square or Monte Carlo method. The proposed procedure also enjoys
good theoretical properties. Moreover, the proposed procedure is not only used
for linear models but also can be used for generalized linear models({\sl
GLM}), Cox models, quantile regression models and many others with the help of
Wang and Leng (2007)'s LSA, which changes these models as the approximation of
linear models. The idea of combining sample and prior constraint information
can be also used for other modified lasso procedures. Some examples are used
for illustration of the idea of incorporating prior constraint information in
variable selection procedures. |
| Palabras clave |
Statistics - Methodology |
| Tipo de recurso |
Texto Narrativo
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| Tipo de Interactividad |
Expositivo
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| Nivel de Interactividad |
muy bajo
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| Audiencia |
Estudiante
Profesor
Autor
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| Estructura |
Atomic |
| Coste |
no
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| Copyright |
sí
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| Requerimientos técnicos |
Browser: Any |
| Fecha de contribución |
26-jun-2007 |
| Contacto |
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