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On some inferential aspects of length biased log-logistic model
International Journal of System Assurance Engineering and Management Pub Date : 2020-08-09 , DOI: 10.1007/s13198-020-01027-1
Ranjita Pandey , Pulkit Srivastava , Neera Kumari

In this paper, we study a weighted distribution which is known to provide adjustment to the base distribution by ascertaining the probability of the actual occurrence of events vis-a-vis records and observations. Log-logistic distribution is a widely used time to event model with heavy tails. We introduce a two parameter length biased log-logistic distribution which is a special case of weighted distribution. Comprehensive description of its various mathematical properties is given. Moment generating function, order statistics and entropy aspects are examined. Stochastic orderings and likelihood ratio are also discussed. The proposed distribution is shown to have a promising potential as a better reliability model. Its advantage over five other popular time to event models is demonstrated through empirical fitting of a classical data set.



中文翻译:

关于长度有偏对数逻辑模型的一些推断方面

在本文中,我们研究了一种加权分布,该加权分布通过确定相对于记录和观察结果实际发生事件的概率来调整基本分布。对数逻辑分布是广泛使用的时间密集事件模型。我们介绍了两个参数长度有偏的对数逻辑分布,这是加权分布的特例。给出了其各种数学性质的全面描述。研究了矩产生函数,阶次统计和熵方面。还讨论了随机排序和似然比。所建议的分布显示出作为更好的可靠性模型具有潜在的潜力。通过对经典数据集进行经验拟合,证明了它比其他五个流行事件模型更具有优势。

更新日期:2020-08-10
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