Statistical Inference of Truncated Cauchy Power-Inverted Topp–Leone Distribution under Hybrid Censored Scheme with Applications

Author:

Mohamed Rania A. H.1,Elgarhy Mohammed2ORCID,Alabdulhadi Manal H.3,Almetwally Ehab M.45ORCID,Radwan Taha36ORCID

Affiliation:

1. Department of Statistics, Mathematics and Insurance, Faculty of Commerce, Port Said University, Port Fouad 42526, Egypt

2. Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni-Suef 62521, Egypt

3. Department of Mathematics, College of Science and Arts, Qassim University, Bukairiayh 51452, Saudi Arabia

4. Faculty of Business Administration, Delta University for Science and Technology, Gamasa 11152, Egypt

5. The Scientific Association for Studies and Applied Research, Al Manzalah 35646, Egypt

6. Department of Mathematics and Statistics, Faculty of Management Technology and Information Systems, Port Said University, Port Said 42511, Egypt

Abstract

In this article, a new two-parameter model called the truncated Cauchy power-inverted Topp–Leone (TCP-ITL) is constructed by merging the truncated Cauchy power -G (TCP-G) family with the inverted Topp–Leone (ITL) distribution. Some structural properties of the newly suggested model are obtained. Different types of entropies are proposed under the TCP-ITL distribution. Under the complete and hybrid censored data, the maximum likelihood (ML), maximum product of spacing (MPSP), and Bayesian estimate approaches are explored. A simulation study is developed to test the proposed distribution’s restricted sample attributes. In the majority of cases, the numerical data revealed that the Bayesian estimates provided more accurate outcomes than the equivalent alternative estimates. The adaptability of the proposed approach is proven using examples from dependability, medicine, and engineering. A real-world data set is utilized to demonstrate the potential of the TCP-ITL distribution in comparison to other well-known distributions. The results of the model selection revealed that the proposed distribution is the best choice for the data sets under consideration.

Publisher

MDPI AG

Subject

Geometry and Topology,Logic,Mathematical Physics,Algebra and Number Theory,Analysis

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