Improving the Efficiency of Regression Estimators in an Inverse Gaussian Regression Model

被引：0

作者：

Rintara, Pannipa ^{[1
]}

Piladaeng, Janjira ^{[2
]}

Ahmed, S. Ejaz ^{[3
]}

Phukongtong, Siwaporn ^{[4
]}

机构：

[1] Kasetsart Univ, Fac Sci & Engn, Dept Gen Sci, Chalermphrakiat Sakon Nakhon Prov Campus, Sakon Nakhon, Thailand

[2] Burapha Univ, Fac Sci, Dept Math, Chon Buri, Thailand

[3] Brock Univ, Dept Math & Stat, St Catharines, ON, Canada

[4] Udon Thani Rajabhat Univ, Fac Sci, Dept Math & Stat, Udon Thani, Thailand

来源：

EIGHTEENTH INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT, ICMSEM 2024 | 2024年 / 215卷

基金：

加拿大自然科学与工程研究理事会;

关键词：

Linear shrinkage; Shrinkage pretest; Inverse Gaussian regression model; Estimation; Simulation;

D O I：

10.1007/978-981-97-5098-6_48

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this research study, the performance of estimation strategies was improved for an inverse Gaussian regression model in the presence of uncertainty regarding the quality of subspace information. It is suggested that the estimation strategies are based on linear shrinkage and shrinkage pretest. To evaluate the relative performance of these estimators, a Monte Carlo simulation for different combinations was conducted. The shrinkage pretest estimation strategy is suggested for use in practical situations, regardless of the quality of subspace information. Furthermore, suggested estimation strategies were applied to a real-data example to evaluate their practicality.

引用

页码：689 / 701

页数：13

共 15 条

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