Search Results - (( time estimation method algorithm ) OR ( data distribution based algorithm ))
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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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Thesis -
2
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 -
3
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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4
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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5
Automatic database of robust neural network forecasting / Saadi Ahmad Kamaruddin, Nor Azura Md. Ghani and Norazan Mohamed Ramli
Published 2014“…The backpropagation algorithm is one of the most famous algorithms to train neural network based on the mean square error (MSE) of ordinary least squares (OLS). …”
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Book Section -
6
Enhancing the QoS performance for mobile station over LTE and WiMAX networks / Mhd Nour Hindia
Published 2015“…In this study, several algorithms are proposed to guarantee high efficiency of resource allocations among a variety of smart grid applications (flat priority based and specific priority based scheduling algorithms), such as distribution automation, voice, video surveillance and advanced metering infrastructure. …”
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7
Robust Kernel Density Function Estimation
Published 2010“…However, the weakness of IRWLS based estimator is that its computation time is very long. …”
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8
Estimation of transformers health index based on condition parameter factor and hidden Markov model
Published 2018“…Next, the emission probabilities for each of the condition parameter factors were derived based on frequency of occurrence method. 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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Conference or Workshop Item -
9
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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10
Statistical modelling of time series of counts for a new class of mixture distributions / Khoo Wooi Chen
Published 2016“…The iv score functions and information matrix have been derived to measure the asymptotic standard errors and to analyze the variance-covariance relationship among the parameters. Parameter estimation with the maximum likelihood estimation via the Expectation-Maximization algorithm is discussed and compared with the conditional least squares method. …”
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11
Competing risks for reliability analysis using Cox’s model
Published 2007“…Design/methodology/approach – The parameters of the models have been estimated by method of maximum likelihood based on EM algorithm. …”
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Article -
12
Density based subspace clustering: a case study on perception of the required skill
Published 2014“…In the early stage, the present research estimates density dimensions and the results are used as input data to determine the initial cluster based on density connection, using DBSCAN algorithm. …”
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13
Density subspace clustering: a case study on perception of the required skill
Published 2014“…In the early stage, the present research estimates density dimensions and the results are used as input data to determine the initial cluster based on density connection, using DBSCAN algorithm. …”
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14
Predicting the popularity of tweets using the theory of point processes.
Published 2019“…The MaSEPTiDE approach shows highly accurate tweet popularity predictions compared to state-of the- art approaches, especially at shorter censoring times. We further propose an inhomogeneous Poisson process model and an estimation method which utilizes internal and external knowledge, based on the times of historical retweets up to the censoring time, and the complete retweet sequences in the training data set respectively. …”
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UMK Etheses -
15
Covariance matrix analysis in simultaneous localization and mapping
Published 2016“…In EKF-based SLAM, measurement data are required at each time step to complete the estimation process. …”
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16
Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model
Published 2000“…A generated data where the failure times were taken as exponentially distributed was used to further compare these two methods of estimation. …”
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17
Vibration-based structural damage detection and system identification using wavelet multiresolution analysis / Seyed Alireza Ravanfar
Published 2017“…Results indicate that the wavelet-based damage detection method precisely identified the location and severity of damages even without reference data. …”
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18
Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis
Published 2004“…A generated data where the failure times are taken as exponentially distributed are used to further compare these two parametric models. …”
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Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani
Published 2018“…To evaluate the performance of the Weibull parameters’ estimator methods, two sets of data are considered, one based on simulated data with different random variable size and the other based on actual data collected from a wind farm in Iran. …”
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