Abstract
In this work, we work around an iterative estimation procedure which has been proposed recently by Lafferty and Wasserman. The procedure is called RODEO and can be used to select the relevant covariates of a sparse regression model. A drawback of the RODEO is that it fails to isolate some relevant covariates, in particular those which have linear effects on the model, and for such reason it is suggested to use the RODEO on the residuals of a LASSO. Here we propose a test which can be integrated to the RODEO procedure in order to fill this gap and complete the final step of the variable selection procedure. A two-stage procedure is therefore proposed. The results of a simulation study show a good performance of the new procedure.
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References
Lafferty, J., Wasserman, L.: RODEO: sparse, greedy nonparametric regression. The Annals of Statistics 36, 28–63 (2008)
Lu, Z.-Q.: Multivariate locally weighted polynomial fitting and partial derivative estimation. Journal of Multivariate Analysis 59, 187–205 (1996)
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© 2014 Springer International Publishing Switzerland
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Giordano, F., Parrella, M.L. (2014). On the RODEO Method for Variable Selection. In: Corazza, M., Pizzi, C. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Cham. https://doi.org/10.1007/978-3-319-02499-8_16
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DOI: https://doi.org/10.1007/978-3-319-02499-8_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02498-1
Online ISBN: 978-3-319-02499-8
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