Search Results - (( parametric estimation method algorithm ) OR ( java application mining algorithm ))
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Semiparametric estimation with profile algorithm for longitudinal binary data
Published 2013“…We use profile algorithm in the estimation of both parametric and nonparametric components. …”
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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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Thesis -
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Direct approach for mining association rules from structured XML data
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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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An intelligent framework for modelling and active vibration control of flexible structures
Published 2004“…Parametric approaches include linear parametric modelling of the system using recursive least squares (RLS) and genetic algorithms (GAS); and non-parametric approaches include multi-layered perceptron neural networks (MLP-NNs) and adaptive neuro-fuzzy inference systems (ANFIS) are employed. …”
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GEE-smoothing spline in semiparametric model with correlated nominal data
Published 2010“…We use profile algorithm in the estimation of both parametric and nonparametric components. …”
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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Semiparametric binary model for clustered survival data
Published 2014“…A backfitting algorithm is used in the derivation of the estimating equation for the parametric and nonparametric components of a semiparametric binary covariate model. …”
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Conference or Workshop Item -
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Analysis of the ECG signal using SVD-based parametric modelling technique
Published 2011“…A two-stage procedure is then used to estimate the EDS model parameters. Prony’s algorithm is first used to obtain initial estimates of the model, while the Gauss-Newton method is applied to solve the non-linear least-square optimisation problem. …”
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Proceeding Paper -
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Parametric cure fraction models for interval-censoring with a change-point based on a covariate threshold
Published 2015“…The proposed model has sound motivation in relapse of cancer and can be used in other disease models. 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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Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor
Published 2017“…This study begins by proposing a robust technique for estimating the slope parameter in LFRM. In particular, the focus is on the non-parametric estimation of the slope parameter and the robustness of this technique is compared with the maximum likelihood estimation and the Al-Nasser and Ebrahem (2005) method. …”
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Estimated and analysis of the relationship between the endogenous and exogenous variables using fuzzy semi-paranetric sample selection model
Published 2014“…Through this approach, a flexible fuzzy concept hybrid with the semi-parametric sample selection models known as Fuzzy Semi-Parametric Sample Selection Model (FSPSSM). …”
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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Conference or Workshop Item -
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A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Final Year Project / Dissertation / Thesis -
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