Search Results - (( data distribution a algorithm ) OR ( (parameter OR parameters) estimation means algorithm ))
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1
Estimation Of Weibull Parameters Using Simulated Annealing As Applied In Financial Data
Published 2023“…However, the selection of the most suitable estimators is still a challenging task. The present study proposes a simulated annealing algorithm (SA) in estimating the parameters of Weibull distribution with application to modified internal rate of return data (MIRR).The objective is to examine the investment potential of the shari’ah compliance companies of the Malaysia property sector (MPS). …”
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2
Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari
Published 2015“…We first look at the concentration parameter of von Mises distribution. The von Mises distribution is the most commonly used probability distribution of a circular random variable, and the concentration of a circular data set is measured using the mean resultant length. …”
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3
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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4
Accuracy enhancement for zone mapping of a solar radiation forecasting based multi-objective model for better management of the generation of renewable energy
Published 2023“…Air quality; Decision making; Forecasting; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Inference engines; Mapping; Mean square error; Multiobjective optimization; Optimal systems; Particle swarm optimization (PSO); Quality control; Renewable energy resources; Solar radiation; Uncertainty analysis; Adaptive neuro-fuzzy inference system; ANFIS; Multi objective algorithm; Multi objective particle swarm optimization; Multi-objective genetic algorithm; Renewable energies; Renewable energy generation; Solar radiation forecasting; Parameter estimation…”
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5
Dynamic Robust Bootstrap Algorithm for Linear Model Selection Using Least Trimmed Squares
Published 2009“…The Ordinary Least Squares (OLS) method is often used to estimate the parameters of a linear model. Under certain assumptions, the OLS estimates are the best linear unbiased estimates. …”
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6
Estimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Model
Published 2023Conference Paper -
7
Parameter estimation of K-distributed sea clutter based on fuzzy inference and Gustafson-Kessel clustering
Published 2011“…This paper proposes a novel approach to estimate the parameters of K-distribution, based on fuzzy Gustafson–Kessel clustering and fuzzy Takagi–Sugeno Kang modelling. …”
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8
Slice sampler algorithm for generalized pareto distribution
Published 2018“…Moreover, the slice sampler algorithm presents a higher level of stationarity in terms of the scale and shape parameters compared with the Metropolis-Hastings algorithm. …”
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9
A new Gompertz-three-parameter-lindley distribution for modeling survival time data
Published 2025“…In this paper, a new survival distribution is introduced. It is a mixture of the Gompertz distribution and three-parameter-Lindley distribution. …”
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10
Estimation of transformers health index based on condition parameter factor and hidden Markov model
Published 2018“…This paper presents a study to estimate future Health Index (HI) of transformer population based on Hidden Markov Model (HMM). …”
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11
Parameter estimation of the cure fraction based on BCH model using left-censored data with covariates.
Published 2011“…The analysis is constructed by means of the exponential distribution in the case of left censoring and within the framework of the expectation maximization (EM) algorithm. …”
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Bayesian survival and hazard estimates for Weibull regression with censored data using modified Jeffreys prior
Published 2013“…In this study, firstly, consideration is given to the traditional maximum likelihood estimator and the Bayesian estimator by employing Jeffreys prior and Extension of Jeffreys prior information on the Weibull distribution with a given shape under right censored data. …”
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13
Statistical approach on grading: mixture modeling
Published 2006“…In the conditional Bayesian model, we assume the data to follow the Normal Mixture distribution where the grades are distinctively separated by the parameters: means and proportions of the Normal Mixture distribution. …”
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14
Air quality measurement using remote sensing and digital images processing techniques / Lim, H. S. … [et al.]
Published 2004“…The newly developed algorithm produced a high degree of accuracy with the correlation coefficient (R) of 0.9001 and the root-mean-square error (RMS) of 5.1008 µg/m³ for PM10. …”
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15
Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model
Published 2000“…From the simulation study for this particular case, we can conclude that the EM algorithm proved to be more superior in terms of mean value of parameter estimates, bias and root mean square error. …”
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16
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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17
Covariance matrix analysis in simultaneous localization and mapping
Published 2016“…In mobile robot SLAM, extended Kalman filter (EKF) has been one of the most preferable estimators due to its relatively simple algorithm and efficiency of the estimation through the representation of the belief by a multivariate Gaussian distribution; unimodal distribution, with a single mean annotated with a corresponding covariance uncertainty. …”
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18
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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19
Bayesian extreme modeling for non-stationary air quality data
Published 2013“…The aim of this paper is to model the non-stationary Generalized Extreme Value distribution with a focus on Bayesian approach. The location parameter is expressed in terms of linear trend over the time period while constant for both scale and shape parameters. …”
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20
Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis
Published 2004“…The Expectation Maximization (EM) algorithm is utilized to obtain the estimate of the parameters in the models. …”
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