Title : ( Double shrunken selection operator )
Authors: B. Yuzba sl , Mohammad Arashi ,
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Abstract
The least absolute shrinkage and selection operator (LASSO) is a prominent estimator which selects significant (under some sense) features and kills insignificant ones. Indeed the LASSO shrinks features larger than a noise level to zero. In this article, we force LASSO to be shrunken more by proposing a Stein-type shrinkage estimator emanating from the LASSO, namely the Stein-type LASSO. The newly proposed estimator proposes good performance in risk sense numerically. Variants of this estimator have smaller relative MSE and prediction error, compared to the LASSO, in the analysis of prostate cancer dataset.
Keywords
, Double shrinking, LASSO, Linear regression model, MSE, Prediction error, Stein-type shrinkage estimator
@article{paperid:1081515,
author = {B. Yuzba Sl and Arashi, Mohammad},
title = {Double shrunken selection operator},
journal = {Communications in Statistics Part B: Simulation and Computation},
year = {2019},
volume = {48},
number = {3},
month = {March},
issn = {0361-0918},
pages = {666--674},
numpages = {8},
keywords = {Double shrinking; LASSO; Linear regression model; MSE; Prediction error; Stein-type shrinkage estimator},
}
%0 Journal Article
%T Double shrunken selection operator
%A B. Yuzba Sl
%A Arashi, Mohammad
%J Communications in Statistics Part B: Simulation and Computation
%@ 0361-0918
%D 2019
