Moment estimation for uncertain regression model with application to factors analysis of grain yield

• Published in: Communications in statistics. Simulation and computation Vol. 53; no. 10; pp. 4936 - 4946
• Main Author: Liu, Yang
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 02.10.2024; Taylor & Francis Ltd
• Subjects: Crop yield; Grain yield; Moment estimation; Parameter estimation; Parameter uncertainty; Regression analysis; Regression models; Uncertain regression analysis; Uncertainty analysis; Uncertainty theory
• ISSN: 0361-0918; 1532-4141
• DOI: 10.1080/03610918.2022.2160461

Uncertain regression analysis is a powerful analytical tool to model the relationships between explanatory variables and the response variable by uncertainty theory. One of the core problems in uncertain regression analysis is to estimate the unknown parameters of an uncertain regression model and the uncertain disturbance term. In this paper, the moment estimation of uncertain regression model is proposed, which can determine both the uncertain regression model and the disturbance term at one time. After that, the uncertain hypothesis test is used to test whether the estimated uncertain regression model is appropriate. Furthermore, a real-world example of factors analysis of grain yield is provided to illustrate the moment estimation. Finally, as a byproduct, this paper also indicates that the stochastic regression model cannot model the agriculture data.