Evaluating performance and determining optimum sample size for regression tree and automatic linear modeling
Yükleniyor...
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Universidade Federal de Minas Gerais
Erişim Hakkı
info:eu-repo/semantics/openAccess
Attribution 3.0 United States
Attribution 3.0 United States
Özet
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
Açıklama
Anahtar Kelimeler
Automatic linear modeling, Biased, Data mining, Regression tree, Simulation
Kaynak
Arquivo Brasileiro de Medicina Veterinaria e Zootecnia
WoS Q Değeri
Q4
Scopus Q Değeri
Cilt
73
Sayı
6
Künye
Genç, S. & Mendeş, M. (2021). Evaluating performance and determining optimum sample size for regression tree and automatic linear modeling. Arquivo Brasileiro de Medicina Veterinaria e Zootecnia, 73(6), 1391–1402. https://doi.org/10.1590/1678-4162-12413
