Evaluating performance and determining optimum sample size for regression tree and automatic linear modeling

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Genc S., Mendes M.

ARQUIVO BRASILEIRO DE MEDICINA VETERINARIA E ZOOTECNIA, vol.73, no.6, pp.1391-1402, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 73 Issue: 6
  • Publication Date: 2021
  • Doi Number: 10.1590/1678-4162-12413
  • Journal Name: ARQUIVO BRASILEIRO DE MEDICINA VETERINARIA E ZOOTECNIA
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, Food Science & Technology Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Page Numbers: pp.1391-1402
  • Çanakkale Onsekiz Mart University Affiliated: Yes

Abstract

This study was carried out for two purposes: comparing performances of Regression Tree and Automatic Linear Modeling and determining optimum sample size for these methods under different experimental conditions. A comprehensive Monte Carlo Simulation Study was designed for these purposes. Results of simulation study showed that percentage of explained variation estimates of both Regression Tree and Automatic Linear Modeling was influenced by sample size, number of variables, and structure of variance covariance matrix. Automatic Linear Modeling had higher performance than Regression Tree under all experimental conditions. It was concluded that the Regression Tree required much larger samples to make stable estimates when comparing to Automatic Linear Modeling.