TY - JOUR
T1 - The Skew-Reflected-Gompertz distribution for analyzing symmetric and asymmetric data
AU - Hoseinzadeh, Akram
AU - Maleki, Mohsen
AU - Khodadadi, Zahra
AU - Contreras-Reyes, Javier E.
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/3/15
Y1 - 2019/3/15
N2 - In this work, we have defined a new family of skew distribution: the Skew-Reflected-Gompertz. We have also derived some of its probabilistic and inferential properties. The maximum likelihood estimates of the proposed distribution parameters are obtained via an EM-algorithm, and performances of the proposed model and its estimates are shown via simulation studies as well as real applications. Three real datasets are also used to illustrate the model performance which can compete against some well-known skew distributions frequently used in applications.
AB - In this work, we have defined a new family of skew distribution: the Skew-Reflected-Gompertz. We have also derived some of its probabilistic and inferential properties. The maximum likelihood estimates of the proposed distribution parameters are obtained via an EM-algorithm, and performances of the proposed model and its estimates are shown via simulation studies as well as real applications. Three real datasets are also used to illustrate the model performance which can compete against some well-known skew distributions frequently used in applications.
KW - EM-algorithm
KW - Finite mixtures
KW - Maximum likelihood estimates
KW - Skew-reflected-Gompertz distribution
KW - Two-piece distributions
UR - http://www.scopus.com/inward/record.url?scp=85054887121&partnerID=8YFLogxK
U2 - 10.1016/j.cam.2018.09.011
DO - 10.1016/j.cam.2018.09.011
M3 - Article
AN - SCOPUS:85054887121
SN - 0377-0427
VL - 349
SP - 132
EP - 141
JO - Journal of Computational and Applied Mathematics
JF - Journal of Computational and Applied Mathematics
ER -