Search Results - (( _ distribution model algorithm ) OR ( parameter estimation model algorithm ))
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1
Semiparametric inference procedure for the accelarated failure time model with interval-censored data
Published 2019“…A computationally simple two-step iterative algorithm, called estimationapproximation algorithm, is introduced for estimating the parameters of the model on the basis of the rank estimators. …”
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An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli
Published 2020“…In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. …”
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Prior selection for Gumbel distribution parameters using multiple-try metropolis algorithm for monthly maxima PM10 data
Published 2013“…Focus is on modelling the extreme data based on block maxima approach using Gumbel distribution. …”
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4
Parameter-driven count time series models / Nawwal Ahmad Bukhari
Published 2018“…Simulation shows that MCEM algorithm and particle method are useful for the parameter estimation of the Poisson model. …”
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5
A parametric mixture model of three different distributions: An approach to analyse heterogeneous survival data
Published 2014“…The parameters estimated by the proposed EM Algorithm scheme are close to the parameters of the postulated model.To investigate the consistency and stability of the EM scheme, the simulations are repeated several times. …”
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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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Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari
Published 2015“…This study focuses on the parameter estimation and outlier detection for some types of the circular model. …”
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9
Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares
Published 2009“…The Ordinary Least Squares (OLS) method is often used to estimate the parameters of a linear model. Under certain assumptions, the OLS estimates are the best linear unbiased estimates. …”
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10
Estimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Model
Published 2023“…Electric transformers; Health; Hidden Markov models; Nonlinear programming; Probability distributions; Quality control; Viterbi algorithm; Condition parameters; Dissolved gas analysis; Distribution transformer; Emission probabilities; Health indices; Non-linear optimization; Remaining useful lives; Transition probabilities; Parameter estimation…”
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11
Adaptive policing and shaping algorithms on inbound traffic using generalized Pareto distribution: article / Nor Azura Ayop
Published 2016“…Log likelihood estimation technique is used to fitted the best 2-parameter CDF compared to WeibuII, Normal and Rician distribution model. …”
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Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization
Published 2022“…This paper compares different initialization methods and investigates their performance and effects on estimating kinetic parameters’ value in models of biological systems. …”
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Determining the order of a moving average model of time series using reversible jump MCMC: a comparison between laplacian and gaussian noises
Published 2020“…After it has worked properly, it was applied to model human heart rate data. The results showed that the MCMC algorithm can estimate the parameters of the MA model. …”
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Adaptive policing and shaping algorithms on inbound traffic using generalized Pareto distribution / Nor Azura Ayop
Published 2016“…Log likelihood estimation technique is used to fit the best 2-parameter CDF compared to Weibull, Normal and Rician distribution model. …”
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15
Kalman filter based impedance parameter estimation for transmission line and distribution line
Published 2019“…Therefore, a detailed study on developing and evaluating the new algorithms for transmission line parameter estimation is considered in this thesis. …”
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Peak time bandwidth control algorithms with fitted traffic model on broadband network YouTube video traffic: article / Aini Azmi
Published 2016“…Maximum Likelihood Estimator (MLE) technique is used to fit the best distribution model. …”
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Peak time bandwidth control algorithms with fitted traffic model on broadband network YouTube video traffic / Aini Azmi
Published 2016“…Maximum Likelihood Estimator (MLE) technique is used to fit the best distribution model. …”
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18
Parameter estimation of Kumaraswamy Burr type X models based on cure models with or without covariates
Published 2017“…In this thesis, we considered two methods via the classical maximum likelihood estimation (MLE) and the Bayes estimation using the Gibbs sampling (G-S) algorithm to estimate the parameters of BKBX, KBX and Beta-Weibull (BWB) models. …”
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19
Rank regression for modeling bus dwell time in the presence of censored observations
Published 2019“…An iterative algorithm is introduced that involves a monotone estimating function of the model parameter, and its minimization is a computationally simple optimization problem. …”
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Estimation of transformers health index based on condition parameter factor and hidden Markov model
Published 2018“…Subsequently, the future states probability distribution was computed based on the HMM prediction model and viterbi algorithm was applied to find the best optimal path sequence of HI for the respective observable condition. …”
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