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Estimation Of Weibull Parameters Using Simulated Annealing As Applied In Financial Data
Published 2023“…The finding reveals that the Weibull distribution is well-suited to describing the investment behaviour of the MPS based on the estimates via the SA algorithm. …”
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Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah
Published 2015“…Analysis results present Weibull Distribution model is the best fitted model. …”
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Time based internet traffic policing and shaping with Weibull traffic model / Mohd Azrul Abdullah
Published 2015“…Analysis results present Weibull Distribution model is the best fitted model. …”
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Bayesian survival and hazard estimates for Weibull regression with censored data using modified Jeffreys prior
Published 2013“…For the Weibull model with right censoring and unknown shape, the full conditional distribution for the scale and shape parameters are obtained via Gibbs sampling and Metropolis-Hastings algorithm from which the survival function and hazard function are estimated. …”
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An analytical approach on parametric estimation of cure fraction based on weibull distribution using interval censored data.
Published 2011“…We propose this cure rate model based on the Weibull distribution with interval censored data. …”
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Variational Bayesian inference for exponentiated Weibull right censored survival data
Published 2023“…The Weibull distribution is on the other hand well-known for modelling monotonic hazard rates. …”
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An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia
Published 2023Conference Paper -
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Cash-flow analysis of a wind turbine operator
Published 2023“…The paper outlines a method to evaluate the distribution of WTG operator's daily cash-flow by developing an algorithm based on Monte-Carlo technique. …”
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Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis
Published 2004“…From the simulation study for this particular case, we can conclude that Weibull distribution describes well the nature of the model concerned as compared to the exponential distribution in terms of the mean value of parameter estimates, bias, and the root means square error. …”
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A simulation study of a parametric mixture model of three different distributions to analyze heterogeneous survival data
Published 2013“…In this paper a simulation study of a parametric mixture model of three different distributions is considered to model heterogeneous survival data.Some properties of the proposed parametric mixture of Exponential, Gamma and Weibull are investigated.The Expectation Maximization Algorithm (EM) is implemented to estimate the maximum likelihood estimators of three different postulated parametric mixture model parameters.The simulations are performed by simulating data sampled from a population of three component parametric mixture of three different distributions, and the simulations are repeated 10, 30, 50, 100 and 500 times to investigate the consistency and stability of the EM scheme.The EM Algorithm scheme developed is able to estimate the parameters of the mixture which are very close to the parameters of the postulated model.The repetitions of the simulation give parameters closer and closer to the postulated models, as the number of repetitions increases, with relatively small standard errors.…”
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Reliability Analysis and Prediction of Time to Failure Distribution of an Automobile Crankshaft
Published 2015“…T, the algorithm has the capability to statistically validate the algorithm to obtain the optimal parametric model to represent the failure of the component against the actual time to failure data from the local automobile industry. …”
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Non-Parametric and Parametric Estimations of Cure Fraction Using Right-and Interval-Censored Data
Published 2011“…The parametric maximum likelihood estimation of the cure fraction was also investigated under the same circumstances considering two scenarios: 1) when covariates were excluded from the analysis. In this case, the estimation was developed based on the exponential and Weibull distributions using the right and interval censoring types; and 2) when covariates were incorporated into the analysis through the scaleparameter of the exponential distribution only using the same types of data censoring. …”
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Some families of count distributions for modelling zero-inflation and dispersion / Low Yeh Ching
Published 2016“…Two new mixed Poisson distributions, namely a three-parameter Poisson-exponentiated Weibull distribution and a fourparameter generalized Sichel distribution is introduced to model over dispersed, zeroinflated and long-tailed count data. …”
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Performance Improvement of Multiobjective Optimal Power Flow-Based Renewable Energy Sources Using Intelligent Algorithm
Published 2022“…Weibull and lognormal probability distribution functions (PDFs) are respectively adopted to describe uncertainties in wind speed and solar irradiance. …”
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Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani
Published 2018“…In this method, firstly, Weibull density function is utilized to model the wind speed and then several methods are applied to estimate the parameters of the wind speed distribution. …”
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Optimization of multipurpose reservoir operation using evolutionary algorithms / Mohammed Heydari
Published 2017“…One of the main problems of this method is premature convergence and to improve this problem, the compound of the particle swarm algorithm and genetic algorithm were evaluated. The basis of this compound is in such a way that the advantages of the Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) have been applied simultaneously. …”
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Visualisasi pohon sintaksis berasaskan model dan algoritma sintaks ayat bahasa Melayu
Published 2018“…It can be concluded that the algorithm and model proposed were useful for the development of the prototype. …”
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Development of warranty management visualisation for automotive aftermarket with integration of AI
Published 2026“…Advanced analytics techniques including Weibull distribution modelling for failure prediction and Python based anomaly detection algorithms were implemented to identify high risk components and unusual claim behaviours. …”
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