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1
Semiparametric inference procedure for the accelarated failure time model with interval-censored data
Published 2019“…The rank-based methods, estimating algorithms, and resampling techniques that are developed do not involve the difficulties of the existing estimating procedures. …”
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2
Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm
Published 2018“…The performance of the algorithm is tested by using simulated data. The test results show that the algorithm can estimate the order and coefficients of the autoregressive model very well. …”
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3
Extreme air pollutant data analysis using classical and Bayesian approaches
Published 2015“…This study started with the analysis of extreme PM10 data based on maximum likelihood estimation technique. …”
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4
Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares
Published 2009“…The performances of the newly proposed methods are investigated extensively by real data sets and simulations study. …”
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5
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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6
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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7
Dynamic replication algorithm in data grid: Survey
Published 2008“…Data replication is a common method used to improve the performance of data access in distributed systems. …”
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8
Kalman filter based impedance parameter estimation for transmission line and distribution line
Published 2019“…To demonstrate the efficiency of the new proposed method, a case study of simulated distribution line in UMP is also considered. …”
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9
An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli
Published 2020“…Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. …”
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10
Fault location estimation for distribution system using simulated voltage sags data
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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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12
Dynamic robust bootstrap method based on LTS estimators
Published 2009“…We call this method Dynamic Robust Bootstrap-LTS based (DRBLTS) because here we have employed the LTS estimator in the modified bootstrap algorithm. …”
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13
Modifying maximum likelihood test for solving singularity and outlier problems in high dimensional cases
Published 2021“…To improve the ML estimators and to maintain the high breakdown point while having high dimensional data sets, a new robust estimator of banded Cholesky and L₁-median with Weiszfeld algorithm, MLѡвсн was suggested. …”
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14
The comparison study among optimization techniques in optimizing a distribution system state estimation
Published 2017“…This thesis introduce an intelligent decentralized State Estimation method based on Firefly algorithm for distribution power systems. …”
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15
Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms
Published 2019“…The utilisation of oil palm biomass as a source of renewable energy is one of the effective methods to promote green energy. Therefore, there is a need to have sufficient data related to oil palm biomass such as yield estimation, oil palm distributions, and locations. …”
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16
Bayesian survival and hazard estimates for Weibull regression with censored data using modified Jeffreys prior
Published 2013“…Next we consider both the scale and shape parameters to be unknown under censored data. It is observed that the estimate of the shape parameter under the maximum likelihood method cannot be obtained in closed form, but can be solved by the application of numerical methods. …”
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17
Voltage and load profiles estimation of distribution network using independent component analysis / Mashitah Mohd Hussain
Published 2014“…The proposed algorithm is tested with IEEE 69 test bus system which represents the distribution part and the method of ICA has been programmed in MATLAB R2012b version. …”
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18
Distribution network fault section estimation using analytical database approach
Published 2004“…This thesis describes the development or a new fault section estimation algorithm in a distribution network. This is the main part of the research which investigates how the voltage sag can be used to estimate the fault section 111 the distribution network. …”
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Parameter estimation of K-distributed sea clutter based on fuzzy inference and Gustafson-Kessel clustering
Published 2011“…The main contribution of the proposed method is the ability to estimate the parameters, given a small number of data which will usually be the case in practical applications. …”
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