Modeling to COVID-19 and cancer data: Using the generalized Burr-Hatke model

被引:2
作者
Ragab, Ibrahim E. [1 ]
Khan, Sadaf [2 ]
Alsadat, Najwan [3 ]
Jamal, Farrukh [2 ]
Hamedani, Gholamhossein G. [4 ]
Elgarhy, Mohammed [5 ,6 ]
机构
[1] Alexandria Acad Management & Accounting, Egyptian Inst, Dept Basic Sci, EIA, Alexandria, Egypt
[2] Islamia Univ Bahawalpur, Dept Stat, Bahawalpur 63100, Pakistan
[3] King Saud Univ, Coll Business Adm, Dept Quantitat Anal, POB 71115, Riyadh 11587, Saudi Arabia
[4] Marquette Univ, Dept Math & Stat Sci, Milwaukee, WI 53233 USA
[5] Beni Suef Univ, Fac Sci, Math & Comp Sci Dept, Bani Suwayf 62521, Egypt
[6] Higher Inst Adm Sci, Dept Basic Sci, Belbeis, Alsharkia, Egypt
关键词
Burr -hatke distribution; Homographic failure rate function; Moments; Residual analysis; Maximum likelihood estimation; Monte Carlo simulation; Characterizations; LOMAX DISTRIBUTION; FAMILY; DISTRIBUTIONS;
D O I
10.1016/j.jrras.2024.100972
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
This paper describes an extended version of the Burr-Hatke distribution via generalizing its survival function. This model is called as the generalized Burr-Hatke distribution. The suggested model's hazard rate function can be non-increasing (decreasing) form. Related statistical characteristics of the proposed model are identified. We provide closed-form expressions for the density function, hazard rate function, moments and their related metrics. In addition, some of the proposed model's reliability attributes, such as mean residual life and mean inactivity time, are determined both empirically and visually. The characterization of GBH model based on hazard rate, truncated and conditional moments is also provided. The maximum likelihood technique is employed for estimating the unknown model parameters. To explore the behavior of the estimates, Monte Carlo Simulation can be used. Finally, the new model's performance is verified using two sets of real data.
引用
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页数:18
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