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A statistical framework for a new Kavya-Manoharan Bilal distribution using ranked set sampling and simple random sampling.
Shafiq, Anum; Sindhu, Tabassum Naz; Riaz, Muhammad Bilal; Hassan, Marwa K H; Abushal, Tahani A.
Affiliation
  • Shafiq A; School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
  • Sindhu TN; IT4Innovations, VSB -Technical University of Ostrava, Ostrava, Czech Republic.
  • Riaz MB; Department of Statistics, Quaid-i-Azam University, Islamabad, 44000, Pakistan.
  • Hassan MKH; IT4Innovations, VSB -Technical University of Ostrava, Ostrava, Czech Republic.
  • Abushal TA; Department of Computer Science and Mathematics, Lebanese American University, Byblos, Lebanon.
Heliyon ; 10(9): e30762, 2024 May 15.
Article in En | MEDLINE | ID: mdl-38765132
ABSTRACT
In survival and stochastic lifespan modeling, numerous families of distributions are sometimes considered unnatural, unjustifiable theoretically, and occasionally superfluous. Here, a novel parsimonious survival model is developed using the Bilal distribution (BD) and the Kavya-Manoharan (KM) parsimonious transformation family. In addition to other analytical properties, the forms of probability density function (PDF) and behavior of the distributions' hazard rates are analyzed. The insights are theoretical as well as practical. Theoretically, we offer explicit equations for the single and product moments of order statistics from Kavya-Manoharan Bilal Distribution. Practically, maximum likelihood (ML) technique, which is based on simple random sampling (SRS) and ranked set sampling (RSS) sample schemes, is employed to estimate the parameters. Numerical simulations are used as the primary methodology to compare the various sampling techniques.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Heliyon Year: 2024 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Heliyon Year: 2024 Document type: Article Affiliation country:
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