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Lazy lasso for local regression

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Author Info

  • Diego Vidaurre

    ()

  • Concha Bielza

    ()

  • Pedro Larrañaga

    ()

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    Abstract

    Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets. Results show that the proposed method outperforms linear and local models for several kinds of scenarios. Copyright Springer-Verlag 2012

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    File URL: http://hdl.handle.net/10.1007/s00180-011-0274-0
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    Bibliographic Info

    Article provided by Springer in its journal Computational Statistics.

    Volume (Year): 27 (2012)
    Issue (Month): 3 (September)
    Pages: 531-550

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    Handle: RePEc:spr:compst:v:27:y:2012:i:3:p:531-550

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    Related research

    Keywords: Lasso; l1-regularization; Variable selection; Loess; Locally weighted regression; Sparse models; Lazy lasso; Nonparametric variable selection;

    References

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    1. Wang, Hansheng & Xia, Yingcun, 2009. "Shrinkage Estimation of the Varying Coefficient Model," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 747-757.
    2. Scott Foster & Arūnas Verbyla & Wayne Pitchford, 2008. "A random model approach for the LASSO," Computational Statistics, Springer, vol. 23(2), pages 217-233, April.
    3. David C Wheeler, 2009. "Simultaneous coefficient penalization and model selection in geographically weighted regression: the geographically weighted lasso," Environment and Planning A, Pion Ltd, London, vol. 41(3), pages 722-742, March.
    4. F. Ferraty & P. Hall & P. Vieu, 2010. "Most-predictive design points for functional data predictors," Biometrika, Biometrika Trust, vol. 97(4), pages 807-824.
    5. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    6. Khan, Jafar A. & Van Aelst, Stefan & Zamar, Ruben H., 2007. "Robust Linear Model Selection Based on Least Angle Regression," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1289-1299, December.
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