Uncertain stochastic ridge estimation in partially linear regression models with elliptically distributed errors
Document Type
Article
Publication Date
1-1-2020
Abstract
In fitting a regression model to survey data, using additional information or prior knowledge, stochastic uncertainty occurs in specifying linear programming due to economic and financial studies. These stochastic constraints, definitely cause some changes in the classic estimators and their efficiencies. In this paper, stochastic shrinkage estimators and their positive parts are defined in the partially linear regression models when the explanatory variables are multicollinear. Also, it is assumed that the errors are dependent and follow the elliptically contoured distribution. The exact risk expressions are derived to determine the relative dominance properties of the proposed estimators. We used generalized cross validation (GCV) criterion for selecting the bandwidth of the kernel smoother and optimal shrinkage parameter. Finally, the Monté-Carlo simulation studies and an application to real world data set are illustrated to support our theoretical findings. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.
Keywords
Uncertain stochastic ridge estimation, kernel smoothing, multicollinearity, partially linear regression model, stein-type shrinkage
Divisions
MathematicalSciences
Funders
A grant No. 266/97/17577 from the Research Councils of Semnan University, Iran,University Malaya Research Grant RP009B–13AFR
Publication Title
Statistics
Volume
54
Issue
3
Publisher
Taylor & Francis