Search Results - (( parametric estimation method algorithm ) OR ( parameter evaluation method algorithm ))
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Non-Parametric and Parametric Estimations of Cure Fraction Using Right-and Interval-Censored Data
Published 2011“…The major research findings were as follows: 1) the non-parametric and parametric estimation methods using the right and interval censoring types produced highly efficient cure rate parameters when the censoring rate was decreased to the minimum possible; 2) Non-parametric estimation of the cure fraction using interval censored data based on Turnbull estimator resulted in more precise cure fraction than the Kaplan Meier estimator considering the interval midpoint to represent the exact life time; 3) The parametric estimation of the cure fraction based on the exponential distribution and right and interval censoring types produced more consistent estimates than the Weibull distribution especially in case of heavy censoring; 4) Parametric estimation of the cure fraction was more efficient when some covariates had been involved in the analysis than when covariates had been excluded; and 5) the nonparametric estimation method is the viable alternative to the parametric one when the data set contains substantial censored observations while in the case of low censoring the parametric method is more attractive.…”
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Analysis of the ECG signal using SVD-based parametric modelling technique
Published 2011“…A two-stage procedure is then used to estimate the EDS model parameters. Prony’s algorithm is first used to obtain initial estimates of the model, while the Gauss-Newton method is applied to solve the non-linear least-square optimisation problem. …”
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Proceeding Paper -
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Semiparametric binary model for clustered survival data
Published 2014“…A backfitting algorithm is used in the derivation of the estimating equation for the parametric and nonparametric components of a semiparametric binary covariate model. …”
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Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications
Published 2018“…The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.…”
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Optimal model order selection for Transient Error Autoregressive Moving Average (TERA) MRI reconstruction method
Published 2008“…These criteria were evaluated on MRI data sets based on the method of Transient Error Reconstruction Algorithm (TERA). …”
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Proceeding Paper -
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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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Optimal model order selection for transient error autoregressive moving average (TERA) MRI reconstruction method
Published 2008“…These criteria were evaluated on MRI data sets based on the method of Transient Error Reconstruction Algorithm (TERA). …”
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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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An In-depth Study of Ankle-Foot Orthosis Dynamics Modeling: Leveraging Non-Parametric Approach Via Artificial Neural Networks
Published 2024“…Subsequently, the model structure was chosen, followed by parameter estimation through the selected algorithm. …”
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Proceeding -
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Semiparametric estimation with profile algorithm for longitudinal binary data
Published 2013“…We use profile algorithm in the estimation of both parametric and nonparametric components. …”
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Parametric maximum likelihood estimation of cure fraction using interval-censored data
Published 2013“…The parametric maximum likelihood estimation method was used for estimation of the cure fraction based on application of the bounded cumulative hazard (BCH) model to interval-censored data. …”
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SYSTEM IDENTIFICATION AND PARAMETRIC ESTIMATION OF INFERENTIAL CORIOLIS
Published 2009“…When experiment was conducted, mass flowrate was measured using inferential coriolis and a commercial flowmeter from a manufacturer i.e., Micro Motion. To validate both methods, a load cell was used as the reference. Details evaluations of three pressure flow scenarios namely the single pressure flow, the continuous pressure flow, and the multi pressure flow for a CNG refueling system are presented. …”
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An intelligent framework for modelling and active vibration control of flexible structures
Published 2004“…Parametric approaches include linear parametric modelling of the system using recursive least squares (RLS) and genetic algorithms (GAS); and non-parametric approaches include multi-layered perceptron neural networks (MLP-NNs) and adaptive neuro-fuzzy inference systems (ANFIS) are employed. …”
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GEE-smoothing spline in semiparametric model with correlated nominal data
Published 2010“…We use profile algorithm in the estimation of both parametric and nonparametric components. …”
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Parametric cure fraction models for interval-censoring with a change-point based on a covariate threshold
Published 2015“…The proposed model has sound motivation in relapse of cancer and can be used in other disease models. The parametric maximum likelihood estimation method is employed to verify the performance of the MCM within the framework of the expectation-maximization (EM) algorithm while the estimation methods for other models are employed in a simpler and straightforward setting. …”
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Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor
Published 2017“…This study begins by proposing a robust technique for estimating the slope parameter in LFRM. In particular, the focus is on the non-parametric estimation of the slope parameter and the robustness of this technique is compared with the maximum likelihood estimation and the Al-Nasser and Ebrahem (2005) method. …”
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Modelling and Control of Ankle Foot Orthosis (AFO) for Children Utilising Soft Computing Towards Intelligent Approach
Published 2024“…The research uses both non-parametric and parametric techniques, namely Multilayer Perceptron Neural Network (MLP NN) and Particle Swarm Optimisation (PSO), for system modelling. …”
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