TY - JOUR
T1 - A New Member of T-X Family with Applications in Different Sectors
AU - Shah, Zubir
AU - Ali, Amjad
AU - Hamraz, Muhammad
AU - Khan, Dost Muhammad
AU - Khan, Zardad
AU - El-Morshedy, M.
AU - Al-Bossly, Afrah
AU - Almaspoor, Zahra
N1 - Publisher Copyright:
© 2022 Zubir Shah et al.
PY - 2022
Y1 - 2022
N2 - This paper proposes a member of the T-X family that incorporates heavy-tailed distributions, known as "a new exponential-X family of distribution."As a special case, the paper studies a submodel of the proposed class named a "new exponential Weibull (NEx-Wei) distribution."Some mathematical properties including hazard rate function, ordinary moments, moment generating function, and order statistics are discussed. Furthermore, we adopt the method of MLE (maximum likelihood estimation) for estimating its model parameters. A brief Monte Carlo simulation study is conducted to evaluate the performances of the MLEs based on biases and mean square error. Finally, we provide a comprehensive study to illustrate the introduced approach by analyzing three real data sets from different disciplines. The analytical goodness of fit measure of the proposed distribution is compared with other well-known distributions. We hope that the proposed class may produce many more new distributions for fitting monotonic and nonmonotonic data in the field of reliability analysis and survival analysis as well.
AB - This paper proposes a member of the T-X family that incorporates heavy-tailed distributions, known as "a new exponential-X family of distribution."As a special case, the paper studies a submodel of the proposed class named a "new exponential Weibull (NEx-Wei) distribution."Some mathematical properties including hazard rate function, ordinary moments, moment generating function, and order statistics are discussed. Furthermore, we adopt the method of MLE (maximum likelihood estimation) for estimating its model parameters. A brief Monte Carlo simulation study is conducted to evaluate the performances of the MLEs based on biases and mean square error. Finally, we provide a comprehensive study to illustrate the introduced approach by analyzing three real data sets from different disciplines. The analytical goodness of fit measure of the proposed distribution is compared with other well-known distributions. We hope that the proposed class may produce many more new distributions for fitting monotonic and nonmonotonic data in the field of reliability analysis and survival analysis as well.
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U2 - 10.1155/2022/1453451
DO - 10.1155/2022/1453451
M3 - Article
AN - SCOPUS:85137944568
SN - 2314-4629
VL - 2022
JO - Journal of Mathematics
JF - Journal of Mathematics
M1 - 1453451
ER -