Open AccessOne Generalized Mixture Pareto Distribution and Estimation of the Parameters by the EM Algorithm for Complete and Right-Censored Data
Author(s) -
Mohamed Kayid
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3125456
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
A new mixture generalized Pareto distribution is introduced. Then, some of its attributes are explored. The maximum likelihood method and expectation maximization (EM) algorithm have been applied to estimate the parameters for complete and right-censored data. In a simulation study, the bias, absolute bias and mean squared error of the maximum likelihood estimator are compared with those related to the EM estimator. The results show that the absolute bias and mean squared error of the EM estimator are smaller than the related values for the maximum likelihood estimator. Finally, to illustrate its usefulness, the model has been applied to describe real data sets.