scholarly journals Vine copula based likelihood estimation of dependence patterns in multivariate event time data

2018 ◽  
Vol 117 ◽  
pp. 109-127 ◽  
Author(s):  
Nicole Barthel ◽  
Candida Geerdens ◽  
Matthias Killiches ◽  
Paul Janssen ◽  
Claudia Czado
2018 ◽  
Vol 7 (1) ◽  
pp. 73-83
Author(s):  
Farhah Izzatul Jannah ◽  
Sudarno Sudarno ◽  
Alan Prahutama

Reliability analysis is the analysis of the possibility that the product or service will function properly for a certain period of time under operating conditions without failure. One configuration of components that can be formed is a series-parallel system on a filter capacitor circuit using 4 components consisting of 2 rectifier diodes, 1 capacitor, and 1 load resistor. The data used to obtain the value of system reliability is the time of failure based on the assumption of failure of the independent component. The function of the form on the system can be expressed by Ф(x)= x1x3 + x1x4 + x2x3 + x2x4 - x1x3x4 - x2x3x4 - x1x2x3 - x1x2x4 + x1x2x3x4. The parameter values of each distribution are calculated using the Median Rank Regression Estimation (MRRE) and Maximum Likelihood Estimation (MLE) methods. To test the data following a certain distribution or not, the calculation is manually done with the Anderson-Darling (AD) test so that it is known that the failure time data of rectifier diode 1 follows the weibull distribution with parameters  and , failure time data of rectifier diode 2 follows weibull distribution with parameters  and , failure time data of capacitors follow normal distribution with parameters  and , and the failure time data of the load resistor following the gamma distribution with parameters  and . From the calculation of system reliability, it shows that the higher the intensity of the system fails it will affect the value of reliability to be lower. A serial system from a parallel system functions if there is at least one component j in one subsystem that functions. Keywords: Reliability, Series-Parallel, MRRE, MLE, AD.


PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258512
Author(s):  
Phillip Oluwatobi Awodutire ◽  
Oluwafemi Samson Balogun ◽  
Akintayo Kehinde Olapade ◽  
Ethelbert Chinaka Nduka

In this work, a new family of distributions, which extends the Beta transmuted family, was obtained, called the Modified Beta Transmuted Family of distribution. This derived family has the Beta Family of Distribution and the Transmuted family of distribution as subfamilies. The Modified beta transmuted frechet, modified beta transmuted exponential, modified beta transmuted gompertz and modified beta transmuted lindley were obtained as special cases. The analytical expressions were studied for some statistical properties of the derived family of distribution which includes the moments, moments generating function and order statistics. The estimates of the parameters of the family were obtained using the maximum likelihood estimation method. Using the exponential distribution as a baseline for the family distribution, the resulting distribution (modified beta transmuted exponential distribution) was studied and its properties. The modified beta transmuted exponential distribution was applied to a real life time data to assess its flexibility in which the results shows a better fit when compared to some competitive models.


2015 ◽  
Vol 75 (1) ◽  
Author(s):  
Zulkarnain Hassan ◽  
Supiah Shamsudin ◽  
Sobri Harun

In selecting the best-fit distribution model for the rainfall event characteristics based on the inter-event time definition (IETD) of 6 hours for the selected rainfall in the Peninsular of Malaysia, seven distributions were utilized namely the beta (B4), exponential (EX1), gamma (G2), generalized extreme value (GEV), generalized Pareto (GP), Log-Pearson 3 (LP3), and Wakeby (WKB). Maximum likelihood estimation (MLE) was applied to estimate the parameters of each distribution.  Based on the results, GP, WKB and GEV were found to be the most suitable distribution for describing the rainfall event characteristics in the studied regions.  


2007 ◽  
Vol 26 (10) ◽  
pp. 2184-2202 ◽  
Author(s):  
Michelle Shardell ◽  
Daniel O. Scharfstein ◽  
Samuel A. Bozzette

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