Improved new modified Weibull distribution: A Bayes study using Hamiltonian Monte Carlo simulation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10243847" target="_blank" >RIV/61989100:27240/20:10243847 - isvavai.cz</a>
Result on the web
<a href="https://journals.sagepub.com/doi/pdf/10.1177/1748006X19896740" target="_blank" >https://journals.sagepub.com/doi/pdf/10.1177/1748006X19896740</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1177/1748006X19896740" target="_blank" >10.1177/1748006X19896740</a>
Alternative languages
Result language
angličtina
Original language name
Improved new modified Weibull distribution: A Bayes study using Hamiltonian Monte Carlo simulation
Original language description
The newly modified Weibull distribution defined in the literature is a model based on combining the Weibull and modified Weibull distributions. It has been demonstrated as the best model for fitting to the bathtub-shaped failure rate data sets. However, another new model based on combining the modified Weibull and Gompertz distributions has been demonstrated later to be even better than the first model. In this article, we have shown how to improve the former model into a better model, and more importantly, we have provided a full Bayesian analysis of the improved model. The Hamiltonian Monte Carlo and cross-entropy methods have been exploited to empower the traditional methods of statistical estimation. Bayes estimators have been obtained using Hamiltonian Monte Carlo for posterior simulations. Bayesian model checking has also been provided in order to check the validation of the model when fitting to real data sets. We have also provided the maximum likelihood estimators of the model parameters using the cross-entropy method to optimize the log-likelihood function. The results derived from the analysis of two well-known data sets show that the improved model is much better than its original form.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: A Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Risk and Reliability
ISSN
1748-006X
e-ISSN
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Volume of the periodical
234
Issue of the periodical within the volume
3
Country of publishing house
GB - UNITED KINGDOM
Number of pages
16
Pages from-to
496-511
UT code for WoS article
000509441700001
EID of the result in the Scopus database
2-s2.0-85078333595