Classical and Bayesian Estimations for the Dagum Distribution
Pavitra Kumari
Chaudhary Charan Singh Haryana Agricultural University, Hisar, India.
Pardeep Kumar *
Chaudhary Charan Singh Haryana Agricultural University, Hisar, India.
Rohit Kundu
Chaudhary Charan Singh Haryana Agricultural University, Hisar, India.
*Author to whom correspondence should be addressed.
Abstract
The Dagum distribution is a great tool for survival analysis as well as representing the distribution of actuarial, meteorological, and income data. Additionally, it is frequently thought to be the best option when compared to the other three parameter distributions. The inverse Burr distribution is a generalised Beta distribution that is produced from generalised beta-II by taking shape parameter one into consideration. The many characteristics and several techniques for estimating the unknown parameters of parameter Dagum distribution are covered in this article. Although we are able to estimate the parameter of the Dagum distribution using Bayesian methods with both informative and noninformative priors, the methods used to produce the estimators are different. Risk functions are used to compare these estimators.
Keywords: Dagum distribution, Bayesian estimation, MLE method of moment estimators, Jeffreys prior, loss functions, risk function