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  • Search: subject:"Generalized log–logistic distribution"
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Year of publication
Subject
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Bootstrap confdence interval 2 Maximum likelihood estimate 2 Process capability index 2 Type-II generalized log-logistic distribution 2 Bayesian estimation 1 Bootstrap approach 1 Bootstrap-Verfahren 1 Estimation theory 1 Generalized log–logistic distribution 1 MCMC 1 Maximum likelihood estimation 1 Maximum-Likelihood-Schätzung 1 Reversed hazard rate 1 Schätztheorie 1 Shared gamma frailty 1 Statistical distribution 1 Statistische Verteilung 1
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Online availability
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Free 2 Undetermined 1
Type of publication
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Article 3
Type of publication (narrower categories)
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Article 1 Article in journal 1 Aufsatz in Zeitschrift 1
Language
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English 2 Undetermined 1
Author
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Gadde, Srinivasa Rao 2 Prasad, SVSVSV 2 Rosaiah, K. 2 Hanagal, David D. 1 Pandey, Arvind 1
Published in...
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Journal of Industrial Engineering International 1 Journal of industrial engineering international 1 Statistics & Probability Letters 1
Source
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ECONIS (ZBW) 1 EconStor 1 RePEc 1
Showing 1 - 3 of 3
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Bootstrap confidence intervals of CNpk for type-II generalized log-logistic distribution
Gadde, Srinivasa Rao; Rosaiah, K.; Prasad, SVSVSV - In: Journal of Industrial Engineering International 15 (2019) S1, pp. 87-94
quality characteristic follows type-II generalized log-logistic distribution introduced by Rosaiah et al. in Int J Agric Stat …
Persistent link: https://www.econbiz.de/10013470819
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Cover Image
Bootstrap confidence intervals of CNpk for type-II generalized log-logistic distribution
Gadde, Srinivasa Rao; Rosaiah, K.; Prasad, SVSVSV - In: Journal of industrial engineering international 15 (2019), pp. 87-94
quality characteristic follows type-II generalized log-logistic distribution introduced by Rosaiah et al. in Int J Agric Stat …
Persistent link: https://www.econbiz.de/10012164564
Saved in:
Cover Image
Gamma shared frailty model based on reversed hazard rate for bivariate survival data
Hanagal, David D.; Pandey, Arvind - In: Statistics & Probability Letters 88 (2014) C, pp. 190-196
The unknown or unobservable risk factors in the survival analysis cause heterogeneity between the individuals. Frailty models are used in the survival analysis to account for the unobserved heterogeneity in the individual risks to disease and death. In this paper, we suggest the shared gamma...
Persistent link: https://www.econbiz.de/10010752971
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