A novel asymmetric extension of power XLindley distribution: properties, inference and applications to engineering data

被引:1
作者
Alsadat, Najwan [1 ]
Hassan, Amal S. [2 ]
Elgarhy, Mohammed [3 ,4 ]
Nagarjuna, Vasili B. V. [5 ]
Benchiha, Sid Ahmed [6 ]
Gemeay, Ahmed M. [7 ]
机构
[1] King Saud Univ, Coll Business Adm, Dept Quantitat Anal, POB 71115, Riyadh 11587, Saudi Arabia
[2] Cairo Univ, Fac Grad Studies Stat Res, 5 Dr Ahmed Zewail St, Giza 12613, Egypt
[3] Beni Suef Univ, Fac Sci, Math & Comp Sci Dept, Bani Suwayf 62521, Egypt
[4] Higher Inst Adm Sci, Dept Basic Sci, Belbeis, Alsharkia, Egypt
[5] Vellore Inst Technol Andhra Pradesh, Dept Math, Amaravati, India
[6] Univ Djillali Liabes Sidi Bel Abbes, Lab Stat & Stochast Proc, BP 89, Sidi Bel Abbes 22000, Algeria
[7] Tanta Univ, Fac Sci, Dept Math, Tanta, Egypt
关键词
novel; asymmetric; extension; xlindley; distributions; properties; LINDLEY DISTRIBUTION; G FAMILY;
D O I
10.1088/1402-4896/ad77fa
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
It is impossible to overstate the importance of using trigonometric functions appropriately in distribution theory. The main contribution of the research is to construct a flexible trigonometric extension of the power XLindley distribution. More specifically, we build an innovative two-parameter lifetime distribution known as the sine power XLindley distribution (SPXLD) using characteristics from the sine-generated family of distributions. As the main motivational fact, it provides an attractive alternative to the power Lindley, power XLindley, weighted Lindley, and extended power Lindley distributions; it may be better able to model lifetime phenomena presenting data of leptokurtic and platkurtic nature. In contrast to the increasing, decreasing, and reversed-j-shaped hazard rate function, the density exhibits asymmetric shapes with varying peakedness levels. Several significant characteristics are illustrated, including moments, the quantile function, the probability density function in series representation, the stress-strength reliability, and incomplete moments. To analyze the behavior of the suggested distribution, sixteen estimation techniques are applied, such as the maximum likelihood, percentiles, some methods of minimum distances, some methods based on minimum and maximum spacing distances, and the Kolmogorov method. After that, an extensive simulation study and the examination of two survival real datasets are used to show the viability, usefulness, and adaptability of the SPXLD. Relevant goodness of fit criteria demonstrates that the SPXLD fits several current distributions.
引用
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页数:31
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