Open AccessComparison of Some Methods for Estimating Mixture of Linear Regression Models with Application
Author(s) -
Urdak Ibrahim Kareem,
Fadhaa Mezher Hashim
Publication year - 2021
Publication title -
mağallaẗ al-ʿulūm al-iqtiṣādiyyaẗ wa-al-idāriyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2518-5764
pISSN - 2227-703X
DOI - 10.33095/jeas.v27i129.2183
Subject(s) - component (thermodynamics) , mixture model , computer science , regression , linear regression , regression analysis , statistics , mean squared error , data mining , mathematics , artificial intelligence , physics , thermodynamics
A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others in most simulation scenarios according to the integrated mean square error and integrated classification error