TY - JOUR
T1 - A new asymmetric extended family
T2 - Properties and estimation methods with actuarial applications
AU - Aljohani, Hassan M.
AU - Bandar, Sarah A.
AU - Al-Mofleh, Hazem
AU - Ahmad, Zubair
AU - El-Morshedy, M.
AU - Afify, Ahmed Z.
N1 - Publisher Copyright:
© 2022 Aljohani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/10
Y1 - 2022/10
N2 - In the present work, a class of distributions, called new extended family of heavy-tailed distributions is introduced. The special sub-models of the introduced family provide unimodal, bimodal, symmetric, and asymmetric density shapes. A special sub-model of the new family, called the new extended heavy-tailed Weibull (NEHTW) distribution, is studied in more detail. The NEHTW parameters have been estimated via eight classical estimation procedures. The performance of these methods have been explored using detailed simulation results which have been ordered, using partial and overall ranks, to determine the best estimation method. Two important risk measures are derived for the NEHTW distribution. To prove the usefulness of the two actuarial measures in financial sciences, a simulation study is conducted. Finally, the flexibility and importance of the NEHTW model are illustrated empirically using two real-life insurance data sets. Based on our study, we observe that the NEHTW distribution may be a good candidate for modeling financial and actuarial sciences data.
AB - In the present work, a class of distributions, called new extended family of heavy-tailed distributions is introduced. The special sub-models of the introduced family provide unimodal, bimodal, symmetric, and asymmetric density shapes. A special sub-model of the new family, called the new extended heavy-tailed Weibull (NEHTW) distribution, is studied in more detail. The NEHTW parameters have been estimated via eight classical estimation procedures. The performance of these methods have been explored using detailed simulation results which have been ordered, using partial and overall ranks, to determine the best estimation method. Two important risk measures are derived for the NEHTW distribution. To prove the usefulness of the two actuarial measures in financial sciences, a simulation study is conducted. Finally, the flexibility and importance of the NEHTW model are illustrated empirically using two real-life insurance data sets. Based on our study, we observe that the NEHTW distribution may be a good candidate for modeling financial and actuarial sciences data.
UR - https://www.scopus.com/pages/publications/85139380187
U2 - 10.1371/journal.pone.0275001
DO - 10.1371/journal.pone.0275001
M3 - Article
C2 - 36201437
AN - SCOPUS:85139380187
SN - 1932-6203
VL - 17
JO - PLoS ONE
JF - PLoS ONE
IS - 10 October
M1 - e0275001
ER -