Academic literature on the topic 'Weibull distribution'
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Journal articles on the topic "Weibull distribution"
Oluyede, Broderick. "The Gamma-Weibull-G Family of Distributions with Applications." Austrian Journal of Statistics 47, no. 1 (January 30, 2018): 45–76. http://dx.doi.org/10.17713/ajs.v47i1.155.
Full textAlzoubi, Loai, Ahmad Al-Khazaleh, Ayat Al-Meanazel, and Mohammed Gharaibeh. "EPANECHNIKOV-WEIBULL DISTRIBUTION." Journal of Southwest Jiaotong University 57, no. 6 (December 30, 2022): 949–58. http://dx.doi.org/10.35741/issn.0258-2724.57.6.81.
Full textCao, Quang V., and Qinglin Wu. "Characterizing wood fiber and particle length with a mixture distribution and a segmented distribution." Holzforschung 61, no. 2 (March 1, 2007): 124–30. http://dx.doi.org/10.1515/hf.2007.023.
Full textAlmazah, Mohammed M. A., Kalim Ullah, Eslam Hussam, Md Moyazzem Hossain, Ramy Aldallal, and Fathy H. Riad. "New Statistical Approaches for Modeling the COVID-19 Data Set: A Case Study in the Medical Sector." Complexity 2022 (August 19, 2022): 1–9. http://dx.doi.org/10.1155/2022/1325825.
Full textKızılersü, Ayşe, Markus Kreer, and Anthony W. Thomas. "The Weibull distribution." Significance 15, no. 2 (April 2018): 10–11. http://dx.doi.org/10.1111/j.1740-9713.2018.01123.x.
Full textMustafa, Abdelfattah, Beih S. Desouky, and Shamsan AL-Garash. "THE WEIBULL GENERALIZED FLEXIBLE WEIBULL EXTENSION DISTRIBUTION." Journal of Data Science 14, no. 3 (March 5, 2021): 453–78. http://dx.doi.org/10.6339/jds.201607_14(3).0004.
Full textKlakattawi, Hadeel S. "The Weibull-Gamma Distribution: Properties and Applications." Entropy 21, no. 5 (April 26, 2019): 438. http://dx.doi.org/10.3390/e21050438.
Full textAminu Adamu, Abubakar Yahaya, and Hussaini Garba Dikko. "APPLICATIONS OF INVERSE WEIBULL RAYLEIGH DISTRIBUTION TO FAILURE RATES AND VINYL CHLORIDE DATA SETS." FUDMA JOURNAL OF SCIENCES 5, no. 2 (June 22, 2021): 89–99. http://dx.doi.org/10.33003/fjs-2021-0502-479.
Full textMakubate, Boikanyo, Broderick O. Oluyede, Gofaone Motobetso, Shujiao Huang, and Adeniyi F. Fagbamigbe. "The Beta Weibull-G Family of Distributions: Model, Properties and Application." International Journal of Statistics and Probability 7, no. 2 (January 18, 2018): 12. http://dx.doi.org/10.5539/ijsp.v7n2p12.
Full textJain, Kanchan, Neetu Singla, and Suresh Kumar Sharma. "The Generalized Inverse Generalized Weibull Distribution and Its Properties." Journal of Probability 2014 (August 6, 2014): 1–11. http://dx.doi.org/10.1155/2014/736101.
Full textDissertations / Theses on the topic "Weibull distribution"
Pyatrin, D. K., O. V. Kozhokhina, G. Y. Marinchenko, L. V. Blahaia, Д. К. Пятрін, О. В. Кожохіна, Г. Є. Марінченко, and Л. В. Благая. "Weibull distribution avionics application." Thesis, National aviation university, 2021. https://er.nau.edu.ua/handle/NAU/50499.
Full textThe paper deals with the weibull distribution in avionics application. During the operation of aircraft, the events that determine the transition of the product to different technical states occur randomly. Intervals of time of stay of a product in this or that condition have casual values of duration. The Weibull distribution is a fairly flexible function that can well align a variety of failure statistics and can be a model for the reliability of both electronic and mechanical products. The Weibull distribution successfully can be used in reliability engineering and failure analysis.
У тезах розглядається розподіл Вейбулла в застосуванні до авіоніки. Під час експлуатації літальних апаратів події, що визначають перехід виробу в різні технічні стани, відбуваються випадковим чином. Інтервали часу перебування виробу в тому чи іншому стані мають випадкові значення тривалості. Розподіл Вейбулла — це досить гнучка функція, яка може добре узгоджувати різноманітні статистичні дані про відмови та може бути взірцем надійності як електронних, так і механічних виробів. Розподіл Вейбулла може бути використаний в прогнозуванні надійності авіоніки та аналізі відмов.
Hansen, Mary Jo. "Probability of Discrete Failures, Weibull Distribution." DigitalCommons@USU, 1989. https://digitalcommons.usu.edu/etd/7023.
Full textNielsen, Mark A. "Parameter Estimation for the Two-Parameter Weibull Distribution." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2509.
Full textHan, Yi Carpenter Mark. "Location-scale bivariate Weibull distributions for bivariate lifetime modeling." Auburn, Ala., 2005. http://repo.lib.auburn.edu/2006%20Spring/master's/HAN_YI_21.pdf.
Full textCrumer, Angela Maria. "Comparison between Weibull and Cox proportional hazards models." Kansas State University, 2011. http://hdl.handle.net/2097/8787.
Full textDepartment of Statistics
James J. Higgins
The time for an event to take place in an individual is called a survival time. Examples include the time that an individual survives after being diagnosed with a terminal illness or the time that an electronic component functions before failing. A popular parametric model for this type of data is the Weibull model, which is a flexible model that allows for the inclusion of covariates of the survival times. If distributional assumptions are not met or cannot be verified, researchers may turn to the semi-parametric Cox proportional hazards model. This model also allows for the inclusion of covariates of survival times but with less restrictive assumptions. This report compares estimates of the slope of the covariate in the proportional hazards model using the parametric Weibull model and the semi-parametric Cox proportional hazards model to estimate the slope. Properties of these models are discussed in Chapter 1. Numerical examples and a comparison of the mean square errors of the estimates of the slope of the covariate for various sample sizes and for uncensored and censored data are discussed in Chapter 2. When the shape parameter is known, the Weibull model far out performs the Cox proportional hazards model, but when the shape parameter is unknown, the Cox proportional hazards model and the Weibull model give comparable results.
Rodrigues, Cristiane. "Distribuições em série de potências modificadas inflacionadas e distribuição Weibull binominal negativa." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-28062011-095106/.
Full textIn this paper, some result such as moments generating function, recurrence relations for moments and some theorems of the class of modified power series distributions (MPSD) proposed by Gupta (1974) and of the class of inflated modified power series distributions (IMPSD) both at a different point of zero as the zero point are presented. The standard Poisson model, the standard negative binomial model and zero inflated models for count data, ZIP and ZINB, using the techniques of the GLMs, were used to analyse two real data sets together with the normal plot with simulated envelopes. The new distribution Weibull negative binomial (WNB) was proposed. Some mathematical properties of the WNB distribution which is quite flexible in analyzing positive data were studied. It is an important alternative model to the Weibull, and Weibull geometric distributions as they are sub-models of WNB. We demonstrate that the WNB density can be expressed as a mixture of Weibull densities. We provide their moments, moment generating function, plots of the skewness and kurtosis, explicit expressions for the mean deviations, Bonferroni and Lorenz curves, quantile function, reliability and entropy, the density of order statistics and explicit expressions for the moments of order statistics. The method of maximum likelihood is used for estimating the model parameters. The expected information matrix is derived. The usefulness of the new distribution is illustrated in two analysis of real data sets.
Reis, Thaís Carolina Santos dos. "Extensões da Distribuição Weibull Aplicadas na Análise de Séries Climatológicas /." Presidente Prudente, 2017. http://hdl.handle.net/11449/152391.
Full textResumo: Na análise de séries climatológicas, a metodologia conhecida como “análise de frequências” inicia-se, após a verificação da validade de algumas suposições, pela escolha e ajuste de uma distribuição de probabilidade. A etapa mais importante desta análise é a escolha ou seleção da distribuição de probabilidade que melhor descreva o verdadeiro comportamento da variável em estudo. Uma vez adotada uma distribuição de probabilidade que esteja bem ajustada, segundo um ou vários critérios, é de interesse, por exemplo, estimar a probabilidade de que eventos de certa magnitude sejam igualados ou excedidos em T anos. O inverso desta probabilidade é chamado de período de retorno, sendo esta uma medida de extrema importância na avaliação de riscos associados a fenômenos climatológicos. Em princípio, qualquer distribuição de probabilidade com suporte nos números reais positivos pode ser utilizada na descrição do comportamento de séries fluviométricas, pluviométricas, eólicas, entre outras. Em se tratando de séries pluviométricas, formadas, por exemplo, pelas pluviosidades diárias, decendiais, mensais, trimestrais e anuais, as distribuições Gama e Weibull são as mais utilizadas. Nos últimos anos, a partir de métodos específicos, uma infinidade de novas distribuições vêm sendo propostas para a análise de observações contínuas e estritamente positivas, cujas aplicações, em sua grande maioria, restringem-se a dados de sobrevivência e confiabilidade. Nesta dissertação de Mestrado, foram avaliad... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: In the climatological series analysis, a methodology known as “frequency analysis” begins, after the validity of some assumptions, by choice and adjustment of a probability distribution. The most important step of this analysis is the choice or selection of probability distribution that best describes the true behavior of the variable under study. Once a probability distribution, that is well adjusted according to one or several criteria, is adopted, it is of interest, for example, to estimate a probability of events of a certain magnitude that are matched or exceeded in T years. The opposite of this probability is called a return period, which is a measure of extreme importance in the evaluation of risks associated with climatological phenomena. In principle, any probability distribution supported by positive real numbers can be used to describe the behavior of fluviometric, pluviometric and wind series, among others. When it comes to the case of rainfall series, formed, for example, by daily, decendial, monthly, quarterly and annual rainfall, the Gamma and Weibull Distributions are more used. In recent years, from specific methods, a plethora of new distributions are being proposed for an analysis of continuous and strictly positive observations, which applications, for the most part, are restricted to survival and reliability data. In this Master’s dissertation, the performances of the Odd Weibull, Marshall-Olkin Weibull, Exponentiated Weibull and Transmutated Weibull Dist... (Complete abstract click electronic access below)
Mestre
Carrasco, Jalmar Manuel Farfán. "Modelo de regressão log-Weibull modificado e a nova distribuição Weibull modificada generalizada." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-29022008-151018/.
Full textIn this paperwork are proposed a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider a classic and Jackknife estimator for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under diferent perturbation schemes and we also present some ways to perform global influence. Besides, for diferent parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the deviance modified residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extend for a martingale-type residual in log-modifiedWeibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the deviance modified residual are performed to select an appropriate model. A new four-parameter distribution is introduced. Various properties the new distribution are discussed. Illustrative examples based on real data are also given.
Watkins, Adam Christopher. "RADIATION INDUCED TRANSIENT PULSE PROPAGATION USING THE WEIBULL DISTRIBUTION FUNCTION." OpenSIUC, 2012. https://opensiuc.lib.siu.edu/theses/811.
Full textAbeyratne, Anura T. "Comparison of k-Weibull populations under random censoring /." free to MU campus, to others for purchase, 1996. http://wwwlib.umi.com/cr/mo/fullcit?p9737910.
Full textBooks on the topic "Weibull distribution"
Dodson, Bryan. Weibull analysis. Milwaukee, Wis: ASQC Quality Press, 1994.
Find full textDodson, Bryan. Weibull analysis. Milwaukee, Wis: ASQC Quality Press, 1994.
Find full textMurthy, D. N. P. Weibull models. Hoboken, N.J: J. Wiley, 2004.
Find full textThe Weibull distribution: A handbook. Boca Raton, Fla: Chapman & Hall/CRC, 2008.
Find full textAbernethy, Robert B. The new Weibull handbook. 3rd ed. North Palm Beach, Fla: R.B. Abernethy, 1998.
Find full textAbernethy, Robert B. The new Weibull handbook. 2nd ed. North Palm Beach, Fla: R.B. Abernethy, 1996.
Find full textMcCool, John. Using the Weibull distribution: Reliability, modeling, and inference. Hoboken, N.J: John Wiley & Sons, 2012.
Find full textYuhai, Mao, and Institution of Electrical Engineers, eds. Weibull radar clutter. London, UK: P. Peregrinus Ltd. on behalf of the Institution of Electrical Engineers, 1990.
Find full textMcCool, John. Using the Weibull distribution: Reliability, modeling, and inference. Hoboken, N.J: John Wiley & Sons, 2012.
Find full textSavage, M. Transmission overhaul estimates for partial and full replacement at repair. [Washington, DC]: National Aeronautics and Space Administration, 1991.
Find full textBook chapters on the topic "Weibull distribution"
Singh, Vijay P. "Weibull Distribution." In Entropy-Based Parameter Estimation in Hydrology, 184–201. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-017-1431-0_12.
Full textLai, Chin-Diew. "Weibull Distribution." In Generalized Weibull Distributions, 1–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39106-4_1.
Full textPadgett, William J. "Weibull Distribution." In International Encyclopedia of Statistical Science, 1651–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_611.
Full textSekine, Matsuo. "Weibull Distribution in Radar Polarimetry." In Direct and Inverse Methods in Radar Polarimetry, 977–88. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-010-9243-2_40.
Full textHasofer, A. M. "A Matrix-Valued Weibull Distribution." In Probabilistic Methods in the Mechanics of Solids and Structures, 11–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82419-7_2.
Full textNguyen-Schäfer, Hung. "Reliability Using the Weibull Distribution." In Computational Design of Rolling Bearings, 141–70. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27131-6_7.
Full textOkagbue, Hilary I., Muminu O. Adamu, Abiodun A. Opanuga, Jimevwo G. Oghonyon, and Patience I. Adamu. "3-Parameter Weibull Distribution: Ordinary Differential Equations." In Transactions on Engineering Technologies, 377–88. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2191-7_27.
Full textWang, Jui-Pin. "Earthquake Recurrence Law and the Weibull Distribution." In Encyclopedia of Earthquake Engineering, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-36197-5_98-1.
Full textWang, Jui-Pin. "Earthquake Recurrence Law and the Weibull Distribution." In Encyclopedia of Earthquake Engineering, 800–806. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35344-4_98.
Full textAli Quadri, Sarfraz, Dhananjay R. Dolas, and Varsha D. Jadhav. "Weibull Distribution Parameters Estimation Using Computer Software." In Artificial Intelligence in Information and Communication Technologies, Healthcare and Education, 213–19. New York: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003342755-21.
Full textConference papers on the topic "Weibull distribution"
Magfira, D. A., D. Lestari, and S. Nurrohmah. "Weibull Lindley distribution." In PROCEEDINGS OF THE 6TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2020 (ISCPMS 2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0059262.
Full textMktof, Abass Habib, Nabeel J. Hassan, and Hassan Kamil Jassim. "Weibull Lindley Pareto distribution." In 3RD INTERNATIONAL SCIENTIFIC CONFERENCE OF ALKAFEEL UNIVERSITY (ISCKU 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0073672.
Full textRenyan Jiang and Tianjun Wang. "Log-Weibull distribution as a lifetime distribution." In 2013 International Conference on Quality, Reliability, Risk, Maintenance and Safety Engineering (QR2MSE). IEEE, 2013. http://dx.doi.org/10.1109/qr2mse.2013.6625694.
Full textBasavalingappa, Adarsh, Jennifer M. Passage, Ming Y. Shen, and J. R. Lloyd. "Electromigration: Lognormal versus Weibull distribution." In 2017 IEEE International Integrated Reliability Workshop (IIRW). IEEE, 2017. http://dx.doi.org/10.1109/iirw.2017.8361224.
Full textCaron, Renault, Adriano Polpo, Paul M. Goggans, and Chun-Yong Chan. "Binary data regression: Weibull distribution." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: The 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2009. http://dx.doi.org/10.1063/1.3275613.
Full textAl-Noor, Nadia Hashim, Salah Hamza Abid, and Mohammad Abd Alhussein Boshi. "On the exponentiated Weibull distribution." In THIRD INTERNATIONAL CONFERENCE OF MATHEMATICAL SCIENCES (ICMS 2019). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5136220.
Full textClement, Nadia L., and Ronald C. Lasky. "Weibull Distribution and Analysis: 2019." In 2020 Pan Pacific Microelectronics Symposium (Pan Pacific). IEEE, 2020. http://dx.doi.org/10.23919/panpacific48324.2020.9059313.
Full textAdnan, Hind, Nabeel J. Hassan, and Hassan Kamil Jassim. "The Weibull Lindley Rayleigh distribution." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING ICCMSE 2021. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0117291.
Full textPodeang, Krittaya, and Winai Bodhisuwan. "Two-sided Topp-Leone Weibull distribution." In PROCEEDINGS OF THE 13TH IMT-GT INTERNATIONAL CONFERENCE ON MATHEMATICS, STATISTICS AND THEIR APPLICATIONS (ICMSA2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5012253.
Full textAbid, Salah Hamza, Nadia Hashim Al-Noor, and Mohammad Abd Alhussein Boshi. "On the generalized inverse Weibull distribution." In CURRENT TRENDS IN RENEWABLE AND ALTERNATE ENERGY. Author(s), 2019. http://dx.doi.org/10.1063/1.5095087.
Full textReports on the topic "Weibull distribution"
Evans, James W., Richard A. Johnson, and David W. Green. Forest Products Laboratory contributions to the use of Weibull distribution in wood engineering. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2019. http://dx.doi.org/10.2737/fpl-gtr-271.
Full textVerrill, Steve P., James W. Evans, David E. Kretschmann, and Cherilyn A. Hatfield. Small Sample Properties of Asymptotically Efficient Estimators of the Parameters of a Bivariate Gaussian–Weibull Distribution. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2012. http://dx.doi.org/10.2737/fpl-rp-667.
Full textVerrill, Steve P., David E. Kretschmann, and James W. Evans. Maximum likelihood estimation of the parameters of a bivariate Gaussian-Weibull distribution from machine stress-rated data. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2016. http://dx.doi.org/10.2737/fpl-rp-685.
Full textLaney, Culbert B. Transformation and Self-Similarity Properties of Gamma and Weibull Fragment Size Distributions. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ada624878.
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