Search Results - (( data simulation based algorithm ) OR ( data estimation method algorithm ))
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1
Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data
Published 2022“…The simulation study results and real data sets indicate that the proposed MRFCHCS+LAD-SCAD estimator was found to be the best method compared to other methods in this study.…”
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Thesis -
2
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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3
Multiple equations model selection algorithm with iterative estimation method
Published 2016“…Therefore, the algorithm is known as SUREIFGLS-Autometrics.Simulation and real data analyses were conducted to assess the performance of the algorithm.The simulation results reveal that the algorithm shows almost similar performances for all conditions involved. …”
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Article -
4
System identification using Extended Kalman Filter
Published 2017“…Besides, Extended Kalman Filter (EKF) algorithm was selected in this project as an algorithm for offline estimation data purposes. …”
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Student Project -
5
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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6
Reduced rank technique for joint channel estimation and joint data detection in TD-SCDMA systems
Published 2013“…The adopted reduced rank technique is based on singular value decomposition algorithm. …”
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7
Reduced Rank Technique for Joint Channel Estimation and Joint Data Detection in TD-SCDMA Systems
Published 2012“…The adopted reduced rank technique is based on singular value decomposition algorithm. …”
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8
Reduced-rank technique for joint channel estimation in TD-SCDMA systems.
Published 2013“…The adopted reduced rank technique is based on singular value decomposition algorithm. …”
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9
Analysis of multiexponential transient signals using interpolation-based deconvolution and parametric modeling techniques
Published 2003“…The performance of the proposed algorithm in accurately estimating the number of exponential signals and their corresponding exponential consfanis for both simulated and real data is investigated in this paper. …”
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Proceeding Paper -
10
Extended multiple models selection algorithms based on iterative feasible generalized least squares (IFGLS) and expectation-maximization (EM) algorithm
Published 2019“…The empirical results for both algorithms performed well as compared to other models selection procedures, particularly using WQI data where the sample size is bigger and has good quality data. …”
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11
Robust estimation methods for fixed effect panel data model having block-concentrated outliers
Published 2019“…Results of simulation study and real data identify RWMM and RWGM to provide more resistant and efficient estimates under MM-centering compare to the existing estimation based on median centering. …”
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12
APPLICATION OF BURG’S ALGORITHM IN STATE ESTIMATION
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Conference or Workshop Item -
13
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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Article -
14
Application of Evolutionary Algorithm for Assisted History Matching
Published 2014“…Besides, algorithm based method has been widely used to forecast future result in various field for example art, biology, marketing including engineering. …”
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Final Year Project -
15
Simulation-Based Power Estimation for High Throughput SHA-256 Design on Unfolding Transformation
Published 2022“…Several cryptographic SHA-256 hash algorithms have been developed to enhance the performance of data-protection techniques. …”
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Proceeding -
16
Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…This merit is provided by balancing the exploitation of solution structure and exploration of its appropriate weighting factors through use of a robust and efficient optimization algorithm in learning process of GEP approach. To assess the applicability and accuracy of the proposed method for long-term electrical energy consumption, its estimates are compared with those obtained from artificial neural network (ANN), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), rule-based data mining algorithm, GEP, linear, quadratic and exponential models optimized by particle swarm optimization (PSO), cuckoo search algorithm (CSA), artificial cooperative search (ACS) algorithm and backtracking search algorithm (BSA). …”
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Thesis -
17
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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18
Simultaneous Computation of Model Order and Parameter Estimation for System Identification Based on Gravitational Search Algorithm
Published 2015“…In this paper, a technique termed as Simultaneous Model Order and Parameter Estimation (SMOPE), which is specifically based on Gravitational Search Algorithm (GSA) is proposed to combine model order selection and parameter estimation in one process. …”
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Conference or Workshop Item -
19
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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Article -
20
Detection of multiple outliners in linear regression using nonparametric methods
Published 2004“…The predicted and residual values are obtained from an ordinary least squares fit of the data. The algorithm is described and is shown to perform well on classic multiple outlier data sets. …”
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