New Partially Linear Regression and Machine Learning Models Applied to Agronomic Data

• Published in: Axioms Vol. 12; no. 11; p. 1027
• Main Authors: Rodrigues, Gabriela M., Ortega, Edwin M. M., Cordeiro, Gauss M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: agronomic experimentation; Agronomy; Algorithms; Artificial intelligence; Asymmetry; Comparative analysis; cross validation; Datasets; decision tree; Decision trees; Machine learning; maximum likelihood estimation; Normal distribution; Parameter estimation; Performance prediction; random forest; Random variables; Regression analysis; Regression models; residual analysis; Simulation methods; Statistical analysis
• ISSN: 2075-1680; 2075-1680
• DOI: 10.3390/axioms12111027

Regression analysis can be appropriate to describe a nonlinear relationship between the response variable and the explanatory variables. This article describes the construction of a partially linear regression model with two systematic components based on the exponentiated odd log-logistic normal distribution. The parameters are estimated by the penalized maximum likelihood method. Simulations for some parameter settings and sample sizes empirically prove the accuracy of the estimators. The superiority of the proposed regression model over other regression models is shown by means of agronomic experimentation data. The predictive performance of the new model is compared with two machine learning techniques: decision trees and random forests. These methods achieved similar prediction performance, i.e., none stands out as a better predictor. In this sense, the objective of the research is to choose the best method. If the objective is only predictive, the decision tree can be used due to its simplicity. For inference purposes, the regression model is recommended, which can provide much more information regarding the relationship of the variables under study.