TY - JOUR
T1 - Neutrosophic Structure of the Log-logistic Model with Applications to Medical Data
AU - Shihabeldeen, Hassabelrasul Y.A.
AU - Khan, Zahid
N1 - Publisher Copyright:
© 2024, American Scientific Publishing Group (ASPG). All rights reserved.
PY - 2024
Y1 - 2024
N2 - In practical scenarios, it is common to encounter fuzzy data that contains numerous imprecise observations. The uncertainty associated with this type of data often leads to the use of interval statistical measures and the proposal of neutrosophic versions of probability distributions to better handle such data. This study introduces a new generalized design of the log-logistic distribution within a neutrosophic framework, building upon encouraging applications of this distribution in fields such as economics, engineering, survival analysis, and lifetime modeling. The proposed neutrosophic loglogistic distribution (NLLD) is analyzed in terms of statistical properties, including moments, shape coefficients, and various survival characteristics. To evaluate the performance of the predicted neutrosophic parameters, an estimation procedure is conducted. Finally, the practical application of the proposed model is demonstrated using a sample dataset consisting of 128 bladder cancer patients.
AB - In practical scenarios, it is common to encounter fuzzy data that contains numerous imprecise observations. The uncertainty associated with this type of data often leads to the use of interval statistical measures and the proposal of neutrosophic versions of probability distributions to better handle such data. This study introduces a new generalized design of the log-logistic distribution within a neutrosophic framework, building upon encouraging applications of this distribution in fields such as economics, engineering, survival analysis, and lifetime modeling. The proposed neutrosophic loglogistic distribution (NLLD) is analyzed in terms of statistical properties, including moments, shape coefficients, and various survival characteristics. To evaluate the performance of the predicted neutrosophic parameters, an estimation procedure is conducted. Finally, the practical application of the proposed model is demonstrated using a sample dataset consisting of 128 bladder cancer patients.
KW - Neutrosophic probability
KW - estimation
KW - log-logistic model
KW - uncertain data
UR - https://www.scopus.com/pages/publications/85177877445
U2 - 10.54216/IJNS.230107
DO - 10.54216/IJNS.230107
M3 - Article
AN - SCOPUS:85177877445
SN - 2692-6148
VL - 23
SP - 85
EP - 96
JO - International Journal of Neutrosophic Science
JF - International Journal of Neutrosophic Science
IS - 1
ER -