Sine Power Lindley Distribution with Applications

IF 2 4区计算机科学 Q2 Computer Science

Intelligent Automation and Soft Computing Pub Date : 2022-01-01 DOI:10.32604/iasc.2022.018043

Abdullah M. Almarashi

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引用次数: 0

Abstract

Sine power Lindley distribution (SPLi), a new distribution with two parameters that extends the Lindley model, is introduced and studied in this paper. The SPLi distribution is more flexible than the power Lindley distribution, and we show that in the application part. The statistical properties of the proposed distribution are calculated, including the quantile function, moments, moment generating function, upper incomplete moment, and lower incomplete moment. Meanwhile, some numerical values of the mean, variance, skewness, and kurtosis of the SPLi distribution are obtained. Besides, the SPLi distribution is evaluated by different measures of entropy such as Rényi entropy, Havrda and Charvat entropy, Arimoto entropy, Arimoto entropy, and Tsallis entropy. Moreover, the maximum likelihood method is exploited to estimate the parameters of the SPLi distribution. The applications of the SPLi distribution to two real data sets illustrate the flexibility of the SPLi distribution, and the superiority of the SPLi distribution over some well-known distributions, including the alpha power transformed Lindley, power Lindley, extended Lindley, Lindley, and inverse Lindley distributions.

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正弦功率林德利分布与应用

本文介绍并研究了正弦功率林德利分布(SPLi)，它是对林德利模型进行扩展的一种新的双参数分布。SPLi分布比功率林德利分布更灵活，我们在应用部分展示了这一点。计算了该分布的统计性质，包括分位数函数、矩、矩生成函数、上不完全矩和下不完全矩。同时，得到了SPLi分布的均值、方差、偏度和峰度的一些数值。此外，还采用r尼伊熵、Havrda和Charvat熵、Arimoto熵、Arimoto熵和Tsallis熵等熵测度评价了SPLi的分布。此外，利用极大似然法估计SPLi分布的参数。SPLi分布在两个真实数据集上的应用说明了SPLi分布的灵活性，以及SPLi分布比一些已知分布(包括alpha幂变换Lindley、幂林德利、扩展林德利、林德利和逆林德利分布)的优越性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Intelligent Automation and Soft Computing 工程技术-计算机：人工智能

CiteScore

3.50

自引率

10.00%

发文量

429

审稿时长

10.8 months

期刊介绍： An International Journal seeks to provide a common forum for the dissemination of accurate results about the world of intelligent automation, artificial intelligence, computer science, control, intelligent data science, modeling and systems engineering. It is intended that the articles published in the journal will encompass both the short and the long term effects of soft computing and other related fields such as robotics, control, computer, vision, speech recognition, pattern recognition, data mining, big data, data analytics, machine intelligence, cyber security and deep learning. It further hopes it will address the existing and emerging relationships between automation, systems engineering, system of systems engineering and soft computing. The journal will publish original and survey papers on artificial intelligence, intelligent automation and computer engineering with an emphasis on current and potential applications of soft computing. It will have a broad interest in all engineering disciplines, computer science, and related technological fields such as medicine, biology operations research, technology management, agriculture and information technology.