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Order Statistics from the Power Lindley Distribution and Associated Inference with Application

机译:Power Lindley分布的订单统计以及与应用程序相关的推断

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Power Lindley distribution has been proposed recently by Ghitany et al. (Comput Stat Data Anal 64:20-33,2013) as a simple and useful reliability model for analysing lifetime data. This model provides more flexibility than the Lindley distribution in terms of the shape of the density and hazard rate functions as well as its skewness and kurtosis. For this distribution, exact explicit expressions for single moments, product moments, marginal moment generating functions and joint moment generating functions of each of these order statistics are derived. By using these relations, we have tabulated the expected values, second moments, variances and covariances of order statistics from samples of sizes up to 10 for various values of the parameters. In addition, we use these moments to obtain the best linear unbiased estimates of the location and scale parameters based on Type-Ⅱ right-censored samples. In addition, we carry out some numerical illustrations through Monte Carlo simulations to show the usefulness of the findings. Finally, we apply the findings of the paper to some real data set.
机译:Ghitany等人最近提出了Power Lindley配电。 (Comput Stat Data Anal 64:20-33,2013)作为分析生命周期数据的简单实用的可靠性模型。就密度和危害率函数的形状,偏度和峰度而言,此模型比Lindley分布具有更大的灵活性。对于这种分布,导出了每个阶次统计量的单个矩,乘积矩,边际矩生成函数和联合矩生成函数的精确显式表达式。通过使用这些关系,我们已将各种参数值从大小最大为10的样本中得出的顺序统计的期望值,第二矩,方差和协方差制成表格。此外,基于Ⅱ型右删截样本,我们利用这些矩来获得位置和尺度参数的最佳线性无偏估计。此外,我们通过蒙特卡洛模拟进行了一些数值说明,以显示发现的有用性。最后,我们将本文的发现应用于一些实际数据集。

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