Number of predictors and multicollinearity: What are their effects on error and bias in regression?

The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates of Type I and Type II error, and variance inflation factor (VIF) values produced under various multicollinearity conditions by multiple regressions with two, four, and six predictors. Findings indicate...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Communications in statistics. Simulation and computation Jg. 48; H. 1; S. 27 - 38
Hauptverfasser:	Lavery, Matthew Ryan, Acharya, Parul, Sivo, Stephen A., Xu, Lihua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Philadelphia Taylor & Francis 02.01.2019 Taylor & Francis Ltd
Schlagworte:	Bias Collinearity Computer simulation Error analysis Monte Carlo simulation Monte Carlo simulation study Multicollinearity Multiple regression Parameters Regression analysis Statistical methods
ISSN:	0361-0918, 1532-4141
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!