scholarly journals Beta Exponentiated Modified Weibull Distribution: Properties and Application

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 781 ◽  
Author(s):  
Mirza Naveed Shahzad ◽  
Ehsan Ullah ◽  
Abid Hussanan
Keyword(s):  
Weibull Distribution ◽  
Reliability Analysis ◽  
Numerical Study ◽  
Real Data ◽  
Estimation Methods ◽  
Lifetime Distributions ◽  
Modified Weibull Distribution ◽  
Proposed Model ◽  
Modified Weibull ◽  
Special Case

One of the most prominent statistical distributions is the Weibull distribution. The recent modifications in this distribution have enhanced its application but only in specific fields. To introduce a more generalized Weibull distribution, in this work beta exponentiated modified Weibull distribution is established. This distribution consolidate the exponential, skewed and symmetric shapes into one density. The proposed distribution also contains nineteen lifetime distributions as a special case, which shows the flexibility of the distribution. The statistical properties of the proposed model are derived and discussed, including reliability analysis and order statistics. The hazard function of the proposed distribution can have a unimodal, decreasing, bathtub, upside-down bathtub, and increasing shape that make it effective in reliability analysis. The parameters of the proposed model are evaluated by maximum likelihood and least squares estimation methods. The significance of the beta exponentiated modified Weibull distribution for modeling is illustrated by the study of real data. The numerical study indicates that the new proposed distribution gives better results than other comparable distributions.

scholarly journals Modeling the survival times of the COVID-19 patients with a new statistical model: A case study from China

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254999
Author(s):  
Xiaofeng Liu ◽  
Zubair Ahmad ◽  
Ahmed M. Gemeay ◽  
Alanazi Talal Abdulrahman ◽  
E. H. Hafez ◽  
...  
Keyword(s):  
Weibull Distribution ◽  
Statistical Model ◽  
Medical Science ◽  
Survival Times ◽  
Data Set ◽  
Monte Carlo Simulation Study ◽  
Modified Weibull Distribution ◽  
Related Data ◽  
Proposed Model ◽  
Modified Weibull

Over the past few months, the spread of the current COVID-19 epidemic has caused tremendous damage worldwide, and unstable many countries economically. Detailed scientific analysis of this event is currently underway to come. However, it is very important to have the right facts and figures to take all possible actions that are needed to avoid COVID-19. In the practice and application of big data sciences, it is always of interest to provide the best description of the data under consideration. The recent studies have shown the potential of statistical distributions in modeling data in applied sciences, especially in medical science. In this article, we continue to carry this area of research, and introduce a new statistical model called the arcsine modified Weibull distribution. The proposed model is introduced using the modified Weibull distribution with the arcsine-X approach which is based on the trigonometric strategy. The maximum likelihood estimators of the parameters of the new model are obtained and the performance these estimators are assessed by conducting a Monte Carlo simulation study. Finally, the effectiveness and utility of the arcsine modified Weibull distribution are demonstrated by modeling COVID-19 patients data. The data set represents the survival times of fifty-three patients taken from a hospital in China. The practical application shows that the proposed model out-classed the competitive models and can be chosen as a good candidate distribution for modeling COVID-19, and other related data sets.

scholarly journals The transmuted generalized modified Weibull distribution

Filomat ◽  
2017 ◽  
Vol 31 (5) ◽  
pp. 1395-1412 ◽  
Author(s):  
Gaussm Cordeiro ◽  
Abdus Saboor ◽  
Muhammad Khan ◽  
Serge Provost
Keyword(s):  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Real Data ◽  
Rate Function ◽  
Modified Weibull Distribution ◽  
Positive Data ◽  
Anderson Darling ◽  
Generalized Modified Weibull ◽  
Modified Weibull ◽  
Von Mises

Canada EM [email protected] AU Ortega Edwinm M. AF Universidade de S?o Paulo, Departamento de Ci?ncias Exatas, Piracicaba, Brazil EM [email protected] KW Generalized modifiedWeibull distribution % Goodness-of-fit statistic % Lifetime data % Transmuted family % Weibull distribution KR nema A profusion of new classes of distributions has recently proven useful to applied statisticians working in various areas of scientific investigation. Generalizing existing distributions by adding shape parameters leads to more flexible models. We define a new lifetime model called the transmuted generalized modified Weibull distribution from the family proposed by Aryal and Tsokos [1], which has a bathtub shaped hazard rate function. Some structural properties of the new model are investigated. The parameters of this distribution are estimated using the maximum likelihood approach. The proposed model turns out to be quite flexible for analyzing positive data. In fact, it can provide better fits than related distributions as measured by the Anderson-Darling (A*) and Cram?r-von Mises (W*) statistics, which is illustrated by applying it to two real data sets. It may serve as a viable alternative to other distributions for modeling positive data arising in several fields of science such as hydrology, biostatistics, meteorology and engineering.

An alternative distribution to Lindley and Power Lindley distributions with characterizations, different estimation methods and data applications

2020 ◽  
Vol 70 (4) ◽  
pp. 953-978
Author(s):  
Mustafa Ç. Korkmaz ◽  
G. G. Hamedani
Keyword(s):  
Hazard Function ◽  
Mixture Distribution ◽  
Real Data ◽  
Quantile Function ◽  
Estimation Methods ◽  
Data Sets ◽  
Unknown Parameters ◽  
Lorenz Curves ◽  
Proposed Model ◽  
New Distribution

AbstractThis paper proposes a new extended Lindley distribution, which has a more flexible density and hazard rate shapes than the Lindley and Power Lindley distributions, based on the mixture distribution structure in order to model with new distribution characteristics real data phenomena. Its some distributional properties such as the shapes, moments, quantile function, Bonferonni and Lorenz curves, mean deviations and order statistics have been obtained. Characterizations based on two truncated moments, conditional expectation as well as in terms of the hazard function are presented. Different estimation procedures have been employed to estimate the unknown parameters and their performances are compared via Monte Carlo simulations. The flexibility and importance of the proposed model are illustrated by two real data sets.

General results for the beta-modified Weibull distribution

2011 ◽  
Vol 81 (10) ◽  
pp. 1211-1232 ◽  
Author(s):  
Saralees Nadarajah ◽  
Gauss M. Cordeiro ◽  
Edwin M.M. Ortega
Keyword(s):  
Weibull Distribution ◽  
Modified Weibull Distribution ◽  
Modified Weibull
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