关于xgamma k记录值和相关推断

Q1 Decision Sciences
Masoud Bazari Jamkhaneh, S. M. T. K. MirMostafaee, Marziye Jadidi
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引用次数: 0

摘要

xgamma分布最初是由Sen等人提出的,作为指数模型的另一种分布。x - γ分布呈浴缸形的危险率函数,因此它适用于许多寿命现象。在本文中,我们考虑来自xgamma分布的上k记录值。我们得到了k记录值的矩的精确显式表达式。我们计算最高k条记录的均值、方差和协方差。利用这些计算值,我们可以找到xgamma模型的位置和尺度参数的最佳线性无偏估计量(BLUEs)和最佳线性不变估计量(盟员)。此外,我们还对未来的k记录值进行预测。我们找到了未来k记录值的最佳线性无偏预测器(BLUP)和最佳线性不变预测器(BLIP)。还讨论了另一种线性预测器。进行了模拟研究,以评估所提出的估计器和预测器。为了说明本文理论结果的应用,文中还给出了一个实际的数据实例。在本文的最后,我们将做一些总结。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

On the xgamma k-record values and associated inference

On the xgamma k-record values and associated inference

On the xgamma k-record values and associated inference

The xgamma distribution was first introduced by Sen et al. [1] as an alternative distribution to the exponential model. The xgamma distribution exhibits a bathtub-shaped hazard rate function, so it is suitable for many lifetime phenomena. In this paper, we consider the upper k-record values from the xgamma distribution. We obtain exact explicit expressions for the moments of k-record values. We compute the means, variances, and covariances of the upper k-records. Using these computed values, we can find the best linear unbiased estimators (BLUEs) and the best linear invariant estimators (BLIEs) of the location and scale parameters of the xgamma model. In addition, we work on the prediction of a future k-record value. We find the best linear unbiased predictor (BLUP) and the best linear invariant predictor (BLIP) of a future k-record value. Another linear predictor is also discussed. A simulation study is performed to assess the proposed estimators and predictors. We also present a real data example in order to illustrate the application of the theoretical results of the paper. At the end of the paper, we will provide several concluding remarks.

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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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