Saved in:
Bibliographic Details
Main Author: Guo, Lina
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2407.15638
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866917730791194624
author Guo, Lina
author_facet Guo, Lina
contents In this paper, we consider finite mixture models with modified proportional hazard rates. Sufficient conditions for the usual stochastic order and the hazard order are established under chain majorization. We study stochastic comparisons under different settings of T-transform for various values of chain majorization. We establish usual stochastic order and hazard rate order between two mixture random variables when a matrix of model parameters and mixing proportions changes to another matrix in some mathematical sense. Sufficient conditions for the star order and Lorenz order are established under weakly supermajorization. The results of this paper are illustrated with numerical examples.
format Preprint
id arxiv_https___arxiv_org_abs_2407_15638
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Orderings of the finite mixture with modified proportional hazard rate model
Guo, Lina
Statistics Theory
In this paper, we consider finite mixture models with modified proportional hazard rates. Sufficient conditions for the usual stochastic order and the hazard order are established under chain majorization. We study stochastic comparisons under different settings of T-transform for various values of chain majorization. We establish usual stochastic order and hazard rate order between two mixture random variables when a matrix of model parameters and mixing proportions changes to another matrix in some mathematical sense. Sufficient conditions for the star order and Lorenz order are established under weakly supermajorization. The results of this paper are illustrated with numerical examples.
title Orderings of the finite mixture with modified proportional hazard rate model
topic Statistics Theory
url https://arxiv.org/abs/2407.15638