A Parametric Bootstrap Approach for a One-Way Error Component Regression Model with Measurement Errors-Reference-Cited by-同舟云学术

A Parametric Bootstrap Approach for a One-Way Error Component Regression Model with Measurement Errors

Published:2023-10-04 Issue:19 Volume:11 Page:4165
ISSN:2227-7390
Container-title:Mathematics
language:en
Short-container-title:Mathematics

Author:

Yue Lili¹^ORCID,Shi Jianhong²,Luo Jingxuan³,Lin Jinguan¹

Affiliation:

1. School of Statistics and Data Science, Nanjing Audit University, Nanjing 211815, China

2. School of Mathematics and Computer Science, Shanxi Normal University, Taiyuan 030032, China

3. School of Statistics, Beijing Normal University, Beijing 100875, China

Abstract

In this paper, a one-way error component regression model with measurement errors is considered. The unknown parameter vector is estimated by using the bias-corrected method, and its corresponding asymptotic properties are also developed. For the hypothesis testing problem of the vector of the coefficient parameter in the model, a parametric bootstrap (PB) method is proposed. Under various sample sizes and parameter configurations, the effectiveness of our proposed PB test method is discussed by using some numerical simulations and a real data analysis.

Funder

National Natural Science Foundation of China

National Social Science Foundation of China

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Link

https://www.mdpi.com/2227-7390/11/19/4165/pdf

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