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Hyperbolic Cosine Log-Logistic Distribution and Estimation of Its Parameters by Using Maximum Likelihood, Bayesian and Bootstrap Methods
Abstract:   (121 Views)
In this paper, a new probability distribution based on the family of hyperbolic cosine distributions is proposed, and its various statistical and reliability characteristics are investigated. The new category of HCF distributions is obtained by combining a baseline F distribution with the hyperbolic cosine function (Kharazmi and Saadatinik [9]). Based on the base log-logistics distribution, we introduce a new distribution called HCLL and we derive the various properties of the proposed distribution including  the moments, quantiles, moment generating function, failure rate function, mean residual lifetime, order statistics and stress-strength parameter. Estimation of the parameters of HCLL for a real data set is investigated by using three methods: maximum likelihood, Bayesian and bootstrap (parametric and non-parametric). In addition, in the application section, by using a realistic data set, the superiority of HCLL model to generalized exponential, Weibull, hyperbolic cosine exponential, gamma, weighted    exponential distributions is shown through the different criteria of selection model.                               
 
Keywords: : Hyperbolic cosine function, Log-Logistics distribution, Mean residual lifetime, Maximum likelihood estimation, Bootstrap.
Full-Text [PDF 275 kb]   (63 Downloads)    
Type of Study: Research | Subject: Special
Received: 2017/08/24 | Accepted: 2018/04/9 | Published: 2018/04/9
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Hyperbolic Cosine Log-Logistic Distribution and Estimation of Its Parameters by Using Maximum Likelihood, Bayesian and Bootstrap Methods. Andishe. 2018; 22 (2)
URL: http://andisheyeamari.irstat.ir/article-1-495-en.html


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