Differenced-Based Double Shrinking in Partial Linear Models

Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
View: 277

This Paper With 12 Page And PDF Format Ready To Download

  • Certificate
  • من نویسنده این مقاله هستم

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این Paper:

شناسه ملی سند علمی:

JR_JCSM-1-1_003

تاریخ نمایه سازی: 18 فروردین 1400

Abstract:

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.

Authors

Mina Norouzirad

Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology,Shahrood, Iran

Mohammad Arashi

Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology,Shahrood, Iran

Mahdi Roozbeh

Department of Mathematics, Statistics and Computer sciences, School of Sciences, Semnan University, Semnan, Iran