Bootstrap-based model selection in subset polynomial regression

Autor:	Suparman Suparman, Mohd Saifullah Rusiman
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	Bootstrap algorithm Subset polynomial Regression Model selection Electronic computers. Computer science QA75.5-76.95
Zdroj:	IJAIN (International Journal of Advances in Intelligent Informatics), Vol 4, Iss 2, Pp 87-94 (2018)
Druh dokumentu:	article
ISSN:	2442-6571 2548-3161
DOI:	10.26555/ijain.v4i2.173
Popis:	The subset polynomial regression model is wider than the polynomial regression model. This study proposes an estimate of the parameters of the subset polynomial regression model with unknown error and distribution. The Bootstrap method is used to estimate the parameters of the subset polynomial regression model. Simulated data is used to test the performance of the Bootstrap method. The test results show that the bootstrap method can estimate well the parameters of the subset polynomial regression model.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/fac6a736583f4b3f8717c5266fc548c0 Zobrazit plný text záznamu View record in DOAJ