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:: Volume 22, Issue 2 (3-2018) ::
Andishe 2018, 22(2): 111-123 Back to browse issues page
Hyperbolic Cosine Log-Logistic Distribution and Estimation of Its Parameters by Using Maximum Likelihood Bayesian and Bootstrap Methods
Abstract:   (397 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. ‎Based on the base log-logistics distribution‎, ‎we introduce a new distribution so-called HCLL and 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)‎. ‎We evaluate the efficiency of the maximum likelihood estimation method by Monte Carlo simulation‎.

‎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 359 kb]   (148 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) :111-123
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Volume 22, Issue 2 (3-2018) Back to browse issues page
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