TY - JOUR
ID - 9254
TI - Differenced-Based Double Shrinking in Partial Linear Models
JO - Journal of Data Science and Modeling
JA - JDSM
LA - en
SN - 2676-5926
AU - Norouzirad, Mina
AU - Arashi, Mohammad
AU - Roozbeh, Mahdi
AD - Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology,Shahrood, Iran
AD - Department of Mathematics, Statistics and Computer sciences, School of Sciences, Semnan University, Semnan, Iran
Y1 - 2022
PY - 2022
VL - 1
IS - 1
SP - 21
EP - 32
KW - Double shrinking
KW - Partial linear model, Preliminary test LASSO
KW - Restricted LASSO
KW - Stein-type Shrinkage LASSO
DO - 10.22054/jcsm.2018.33896.1008
N2 - Partial linear model is very flexible when the relation between the covariates and responses, either parametric and nonparametric. However, estimation of the regression coefficients is challenging since one must also estimate the nonparametric component simultaneously. As a remedy, the differencing approach, to eliminate the nonparametric component and estimate the regression coefficients, can be used. Here, suppose the regression vector-parameter is subjected to lie in a sub-space hypothesis. In situations where the use of difference-based least absolute and shrinkage selection operator (D-LASSO) is desired for, we propose a restricted D-LASSO estimator. To improve its performance, LASSO-type shrinkage estimators are also developed. The relative dominance picture of suggested estimators is investigated. In particular, the suitability of estimating the nonparametric component based on the Speckman approach is explored. A real data example is given to compare the proposed estimators. From the numerical analysis, it is obtained that the partial difference-based shrinkage estimators perform better than the difference-based regression model in average prediction error sense.
UR - https://jdscm.atu.ac.ir/article_9254.html
L1 - https://jdscm.atu.ac.ir/article_9254_cd0b3a7fae6869b4ea958253fbfb9ef4.pdf
ER -