A Model Fit Comparative Study of K-Component Mixture of One Parameter Univariate Distributions
Udochukwu Victor Echebiri, Christogonus Ifeanyichukwu Ugoh, Emwinloghosa Kenneth Guobadia, Onaghise Andrew Isibor, Abayomi Omotayo
Asian Journal of Probability and Statistics · pp. 1–8 · Published 17 Oct 2022
10.9734/ajpas/2022/v20i3421Abstract
This is a comparative study on mixture distribution; where the study seeks to ascertain whether higher number of k-component mixtures could result to development of models that show better fits. In the performance comparison, special consideration was given to univariate one parameter distributions derived using mixture models, and the results show that distributions of higher k-mixture components relatively have greater propensity to exhibit better fit than the lesser mixture component distributions (k < 3).
Cited by 1
Udochukwu Victor Echebiri, Emwinloghosa Kenneth Guobadia, Johnpaul Kenechukwu Iwuchukwu · Asian Journal of Probability and Statistics · 2022
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