Search Results - (( interval estimation method algorithm ) OR ( data distribution function algorithm ))
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Semiparametric inference procedure for the accelarated failure time model with interval-censored data
Published 2019“…The findings of this research provide two new iterative algorithms for estimating the parameters of the AFT model with interval-censored data, and also two new resampling techniques for estimating the covariance matrix of estimators. …”
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Non-Parametric and Parametric Estimations of Cure Fraction Using Right-and Interval-Censored Data
Published 2011“…In this thesis, we considered two methods via the expectation maximization (EM) algorithm for cure rate estimation based on the BCH model using the two censoring types common to cancer clinical trials; namely, right and interval censoring. …”
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Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari
Published 2015“…In this study, we propose an alternative method of constructing a confidence interval based from the distribution of the estimated value of error concentration parameter obtained from the Fisher information matrix. …”
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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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Turnbull versus Kaplan-Meier estimators of cure rate estimation using interval censored data
Published 2012“…Thus, the non-parametric estimation methods are employed by means of the EM algorithm. …”
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Non-parametric maximum likelihood estimation of cure fraction for interval survival data.
Published 2011“…The Nonparametric maximum likelihood estimation method is used for interval censored data based on the bounded cumulative hazard (BCH) model. …”
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The computation of confidence intervals for the state parameters of power systems
Published 2016“…Methods: The feasible interval estimates for the system state parameters have been modelled in this study by considering the random uncertainty in the processing measurements. …”
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The computation of confidence intervals for the state parameters of power systems
Published 2016“…Methods: The feasible interval estimates for the system state parameters have been modelled in this study by considering the random uncertainty in the processing measurements. …”
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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. Maximum likelihood estimation (MLE) method is proposed to estimate the parameters within the framework of expectation-maximization (EM) algorithm, Newton Raphson method also employed. …”
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Cure fraction model for interval censoring with a change point based on a covariate threshold
Published 2015“…Maximum likelihood estimators of the model parameters are obtained using the Expectation Maximization (EM) algorithm. …”
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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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Parametric cure fraction models for interval-censoring with a change-point based on a covariate threshold
Published 2015“…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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13
Rank-based inference for the accelerated failure time model in the presence of interval censored data
Published 2016“…In this paper a class of asymptotically normal rank estimators is developed which can be aquired via linear programming for estimating the parameters of the model, and a two-step iterative algorithm is introduce for solving the estimating equations. …”
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Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm
Published 2018“…Design of hydraulic structures, flood warning systems, evacuation measures, and traffic management require river flood routing. A common hydrologic method of flood routing is the Muskingum method. The present study attempted to develop a three-parameter Muskingum model considering lateral flow for flood routing, coupling with a new optimization algorithm namely, Improved Bat Algorithm (IBA). …”
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k- Nearest Neighbor Algorithm For Improving Accuracy In Clutter Based Location Estimation Of Wireless Nodes
Published 2013“…This research is focusing on the precise location estimation of mobile node by using k - nearest neighbor algorithm (k-NN). …”
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Estimation of non-contact smartphone video-based vital sign monitoring using filtering and standard color conversion techniques
Published 2018“…This rPPG signal could perform better as compared to the standard vital signs estimation methods. The Inter beat interval (IBI) based on maximum peak detection approach has been used for heart rate variability estimation. …”
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Voltage and load profiles estimation of distribution network using independent component analysis / Mashitah Mohd Hussain
Published 2014“…The voltage profiles are estimated for 24- hours with time interval of 1 minute. …”
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19
Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm
Published 2018“…In the hierarchical Bayesian approach, the order and coefficients of the autoregressive model are assumed to have a prior distribution. The prior distribution is combined with the likelihood function to obtain a posterior distribution. …”
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Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…Thereafter, a multi-objective hybrid algorithm (MOHA), an extension of the self-adaptive hybrid algorithm is proposed and tested on the established multi-objective (MO) test functions. …”
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