0524681 20240103224115.320200602235959.9 000540209500005 85085638278 10.1016/j.ijar.2020.04.007 22 s. P cav_un_epca*02567740888-613X1231 (2020)55-76Elsevier Non-linear failure rate Bayesian estimators Hamiltonian Monte Carlo cav_un_auth*0101227 20 Volf Petr UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0392672 40 Thach T. CZ cav_un_auth*0265056 20 Briš R. CZ cav_un_auth*0392673 20 Coolen F. GB http://library.utia.cas.cz/separaty/2020/SI/volf-0524681.pdf https://www.sciencedirect.com/science/article/pii/S0888613X20301596 A non-linear failure ratemodel is introduced, analyzed, and applied to real data sets for both censored and uncensored data. The Hamiltonian Monte Carlo and cross-entropy methods have been exploited to empower the traditional methods of statistical estimation. Bayes estimators of parameters and reliability characteristics uses the Hamiltonian Monte Carlo and these estimators are considered under both symmetric and asymmetric loss functions. Additionally, the maximum likelihood estimators of parameters are obtained by using the cross-entropy method to optimize the log-likelihood function. The superiority of the proposed model and estimation procedures are demonstrated on real data sets. WOS BB 10000 10100 10103 2021 4 RVO:67985556 http://hdl.handle.net/11104/0309168 S 3+4 Article Computer Science Artificial Intelligence C COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE 3.089 1.203 6.7 0.00487 0.727 4819 116 1.039 46.71 4.41 2007 Q1 2.861 128 Q2 40.175 68 Q3 Q1 0.78 Q2 67.986 2020 85085638278 SCOPUS PUBMED 000540209500005 WOS cav_un_epca*0256774 International Journal of Approximate Reasoning 0888-613X 1873-4731 Roč. 123 č. 1 2020 55 76 Elsevier