Bayesian Methods and Maximum Likelihood Estimations of Exponential Censored Time Distribution with Cure Fraction

Autor:	Al Omari Mohammed Ahmed
Rok vydání:	2021
Předmět:	Exponential distribution Mean squared error Applied Mathematics Maximum likelihood Statistics Prior probability Bayesian probability Fraction (mathematics) Function (mathematics) Statistics::Computation Mathematics Exponential function
Zdroj:	Academic Journal of Applied Mathematical Sciences. :106-112
ISSN:	2415-2188 2415-5225
DOI:	10.32861/ajams.72.106.112
Popis:	This paper is focused on estimating the parameter of Exponential distribution under right-censored data with cure fraction. The maximum likelihood estimation and Bayesian approach were used. The Bayesian method is implemented using gamma, Jeffreys, and extension of Jeffreys priors with two loss functions, which are; squared error loss function and Linear Exponential Loss Function (LINEX). The methods of the Bayesian approach are compared to maximum likelihood counterparts and the comparisons are made with respect to the Mean Square Error (MSE) to determine the best for estimating the parameter of Exponential distribution under right-censored data with cure fraction. The results show that the Bayesian with gamma prior under LINEX loss function is a better estimation of the parameter of Exponential distribution with cure fraction based on right-censored data.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0a091c2a0a111a7fa43ea8fbd286d06b https://doi.org/10.32861/ajams.72.106.112 Zobrazit plný text záznamu