Abstract
The Burr Type XII distribution is one of the systems of continuous distributions and is widely known because the distribution includes the characteristics of various well known distributions such as Weibull and gamma distributions. Maximum likelihood estimation (MLE) has been a common method in estimating model parameters. In this paper, we present an alternative method that is expectation-maximization (EM) algorithm to estimate the two- and three- parameter Burr Type XII distributions in the presence of complete and censored data. Furthermore, simulation study is conducted to compare the efficiency and accuracy of MLE and EM algorithm. We discover that EM estimation is more efficient and accurate than those estimates obtained via MLE approach.________________________________________GRAPHICAL ABSTRACT
Highlights
The word ‘Burr’ was introduced by [1] in 1942 when a few forms of cumulative distribution function were suggested to fit the data
In his guide to the Dagum distribution (2007), [2] stated that in economics, the Burr Type XII distribution is known as the Singh-Maddala distribution
Several methods have been used to estimate the parameters of the Burr Type XII distribution such as Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE) and Bayesian Estimation
Summary
The word ‘Burr’ was introduced by [1] in 1942 when a few forms of cumulative distribution function were suggested to fit the data. Burr Type XII distribution has at least two unknown parameter. [3] derived the probability density function of a six-parameter generalized Burr Type XII distribution and obtained cumulative distribution function [4] introduced properties of seven parameters Burr Type XII distribution. Estimation process is very important to find the approximate value of unknown parameters. Several methods have been used to estimate the parameters of the Burr Type XII distribution such as Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE) and Bayesian Estimation. The parameter estimation process involves the presence of complete and censored data
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