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Neutrosophic Exponential Distribution: Modeling and Applications for Complex Data Analysis
by
Gulistan, Muhammad
, Khan, Zahid
, Khurshid, Adnan
, Duan, Wen-Qi
in
Data analysis
/ Mathematical models
/ Parameters
/ Probability distribution functions
/ Queuing theory
/ Random variables
/ Reliability engineering
2021
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Neutrosophic Exponential Distribution: Modeling and Applications for Complex Data Analysis
by
Gulistan, Muhammad
, Khan, Zahid
, Khurshid, Adnan
, Duan, Wen-Qi
in
Data analysis
/ Mathematical models
/ Parameters
/ Probability distribution functions
/ Queuing theory
/ Random variables
/ Reliability engineering
2021
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Do you wish to request the book?
Neutrosophic Exponential Distribution: Modeling and Applications for Complex Data Analysis
by
Gulistan, Muhammad
, Khan, Zahid
, Khurshid, Adnan
, Duan, Wen-Qi
in
Data analysis
/ Mathematical models
/ Parameters
/ Probability distribution functions
/ Queuing theory
/ Random variables
/ Reliability engineering
2021
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Neutrosophic Exponential Distribution: Modeling and Applications for Complex Data Analysis
Journal Article
Neutrosophic Exponential Distribution: Modeling and Applications for Complex Data Analysis
2021
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Overview
The exponential distribution has always been prominent in various disciplines because of its wide range of applications. In this work, a generalization of the classical exponential distribution under a neutrosophic environment is scarcely presented. The mathematical properties of the neutrosophic exponential model are described in detail. The estimation of a neutrosophic parameter by the method of maximum likelihood is discussed and illustrated with examples. The suggested neutrosophic exponential distribution (NED) model involves the interval time it takes for certain particular events to occur. Thus, the proposed model may be the most widely used statistical distribution for the reliability problems. For conceptual understanding, a wide range of applications of the NED in reliability engineering is given, which indicates the circumstances under which the distribution is suitable. Furthermore, a simulation study has been conducted to assess the performance of the estimated neutrosophic parameter. Simulated results show that imprecise data with a larger sample size efficiently estimate the unknown neutrosophic parameter. Finally, a complex dataset on remission periods of cancer patients has been analyzed to identify the importance of the proposed model for real-world case studies.
Publisher
Hindawi,John Wiley & Sons, Inc,Wiley
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