Improving the prediction performance of the LASSO by subtracting the additive structural noises

Morteza Amini () and Mahdi Roozbeh ()
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Morteza Amini: University of Tehran
Mahdi Roozbeh: Semnan University

Computational Statistics, 2019, vol. 34, issue 1, No 18, 415-432

Abstract: Abstract It is shown that the prediction performance of the LASSO method is improved for high dimensional data sets by subtracting structural noises through a sparse additive partially linear model. A mild combination of the partial residual estimation method and the back-fitting algorithm by further implying the LASSO method to the predictors of the linear part is proposed to estimate the parameters. The method is applied to the riboflavin production data set and a simulation study is conducted to examine the performance of the proposed method.

Keywords: Additive partially linear model; High dimensional; LASSO; Kernel smoothing; Sparsity (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00180-018-0849-0

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