The Use of the Nonnegative Garrote for Order Selection of ARX Models
نویسندگان
چکیده
Order selection of linear regression models has been thoroughly researched in the statistical community for some time. Different shrinkage methods have been proposed, such as the Ridge and Lasso regression methods. Especially the Lasso regression has won fame because of its ability to set less important parameters exactly to zero. However, these methods do not take dynamical systems into account, where the regressors are ordered via the time lag. To this end, a modified variant of the nonnegative garrote method will be analyzed.
منابع مشابه
The Use of Non-Negative Garrote for Order Selection of arx Models, Report no. LiTH-ISY-R-2876
Order selection of linear regression models has been thoroughly researched in the statistical community for some time. Different shrinkage methods have been proposed, such as the Ridge and Lasso regression methods. Especially the Lasso regression has won fame because of its ability to set less important parameters exactly to zero. However, these methods do not take dynamical systems into accoun...
متن کاملOn the Nonnegative Garrote Estimator
We study the nonnegative garrote estimator from three different aspects: computation, consistency and flexibility. We show that the nonnegative garrote estimate has a piecewise linear solution path. Using this fact, we propose an efficient algorithm for computing the whole solution path for the nonnegative garrote estimate. We also show that the nonnegative garrote has the nice property that wi...
متن کاملRobust nonnegative garrote variable selection in linear regression
Robust selection of variables in a linear regression model is investigated. Many variable selection methods are available, but very few methods are designed to avoid sensitivity to vertical outliers aswell as to leverage points. The nonnegative garrotemethod is a powerful variable selection method, developed originally for linear regression but recently successfully extended to more complex reg...
متن کاملNonnegative Garrote Component Selection in Functional ANOVA models
We consider the problem of component selection in a functional ANOVA model. A nonparametric extension of the nonnegative garrote (Breiman, 1996) is proposed. We show that the whole solution path of the proposed method can be efficiently computed, which, in turn , facilitates the selection of the tuning parameter. We also show that the final estimate enjoys nice theoretical properties given that...
متن کاملVariable Selection in Additive Models by Nonnegative Garrote
We adapt Breiman’s (1995) nonnegative garrote method to perform variable selection in nonparametric additive models. The technique avoids methods of testing for which no reliable distributional theory is available. In addition it removes the need for a full search of all possible models, something which is computationally intensive, especially when the number of variables is moderate to high. T...
متن کامل