AU - Arast, Mohammmad
AU - Arashi, Mohammmad
AU - Rabie, Mohammmad reza
TI - Performance Study of Shrinkage Estimator Under a Linear Constrain in Penalized Regression
PT - JOURNAL ARTICLE
TA - JSS
JN - JSS
VO - 13
VI - 1
IP - 1
4099 - http://jss.irstat.ir/article-1-506-en.html
4100 - http://jss.irstat.ir/article-1-506-en.pdf
SO - JSS 1
AB - Often, in high dimensional problems, where the number of variables is large the number of observations, penalized estimators based on shrinkage methods have better efficiency than the OLS estimator from the prediction error viewpoint. In these estimators, the tuning or shrinkage parameter plays a deterministic role in variable selection. The bridge estimator is an estimator which simplifies to ridge or LASSO estimators varying the tuning parameter. In these paper, the shrinkage bridge estimator is derived under a linear constraint on regression coefficients and its consistency is proved. Furthermore, its efficiency is evaluated in a simulation study and a real example.
CP - IRAN
IN - Department Statistics, University Shahrood, Shahrood , Iran.
LG - eng
PB - JSS
PG - 1
PT - Applied
YR - 2019