一般分布族的两个有限混合的随机比较

IF 0.9 4区 数学 Q3 STATISTICS & PROBABILITY
Metrika Pub Date : 2023-11-20 DOI:10.1007/s00184-023-00930-4
Raju Bhakta, Priyanka Majumder, Suchandan Kayal, Narayanaswamy Balakrishnan
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引用次数: 1

摘要

本文考虑了两种具有一般参数分布族的有限(算术)混合模型。然后,在模型参数向量以p-大阶、倒数最大阶和弱上/下最大阶连接的假设下,建立了通常随机阶和危害率阶的充分条件。在此基础上,从数学意义上建立了由模型参数和混合比例组成的矩阵变为另一个矩阵时,两个混合随机变量(mrv)之间的风险率序和反向风险率序。我们还考虑了分布的尺度族,以建立mrv具有危险率顺序的一些充分条件。给出了几个例子来说明和澄清这里建立的所有结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Stochastic comparisons of two finite mixtures of general family of distributions

Stochastic comparisons of two finite mixtures of general family of distributions

We consider here two finite (arithmetic) mixture models (FMMs) with general parametric family of distributions. Sufficient conditions for the usual stochastic order and hazard rate order are then established under the assumption that the model parameter vectors are connected in p-larger order, reciprocal majorization order and weak super/sub majorization order. Furthermore, we establish hazard rate order and reversed hazard rate order between two mixture random variables (MRVs) when a matrix of model parameters and mixing proportions changes to another matrix in some mathematical sense. We have also considered scale family of distributions to establish some sufficient conditions under which the MRVs have hazard rate order. Several examples are presented to illustrate and clarify all the results established here.

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来源期刊
Metrika
Metrika 数学-统计学与概率论
CiteScore
1.50
自引率
14.30%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Metrika is an international journal for theoretical and applied statistics. Metrika publishes original research papers in the field of mathematical statistics and statistical methods. Great importance is attached to new developments in theoretical statistics, statistical modeling and to actual innovative applicability of the proposed statistical methods and results. Topics of interest include, without being limited to, multivariate analysis, high dimensional statistics and nonparametric statistics; categorical data analysis and latent variable models; reliability, lifetime data analysis and statistics in engineering sciences.
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