Search Results - (( parameter estimation using algorithm ) OR ( risk estimation using algorithmics ))

Estimation Of Weibull Parameters Using Simulated Annealing As Applied In Financial Data by Hamza, Abubakar

“…The performance of the SA algorithm has been explored in terms of accuracies and estimation errors. …”
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Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model by Elfaki, Faiz. A. M.

“…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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Slice sampler algorithm for generalized pareto distribution by Rostami, Mohammad, Adam, Mohd Bakri, Yahya, Mohamed Hisham, Ibrahim, Noor Akma

“…Finally, the slice sampler algorithm was employed to estimate the re- turn and risk values of investment in Malaysian gold market.…”
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Bayesian logistic regression model on risk factors of type 2 diabetes mellitus by Chiaka, Emenyonu Sandra

“…The significant variables determined by maximum likelihood method were then estimated using the BLR method. The BLR approach via Gibbs sampler and the random walk metropolis algorithm suggests that family history of diabetes, waist circumference and the body mass index are the significant risk factors associated with the type 2 diabetes mellitus. …”
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Assessment of the beach profile at Terengganu coastline / Emran Zaki Abdul Halim by Abdul Halim, Emran Zaki

“…The objectives include determining the beach profile of the Terengganu coastline using Google Earth in 2023, generating coastal slope estimates and beach profiles using a slope algorithm in 2023, and analyzing potential risk areas along the Kuala Terengganu to Marang coastline. …”
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Extreme air pollutant data analysis using classical and Bayesian approaches by Mohd Amin, Nor Azrita

“…MTM algorithm is an extension of MH algorithm, designed to improve the convergence of MH algorithm by performing parallel computation. …”
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Statistical process control for failure crushing time data using competing risks model by Elfaki, Faiz Ahmed Mohamed, Daoud, Jamal Ibrahim, Azram, Mohammad, Daud, Isa, Ibrahim, N.A., Usman, Mustofa

“…EM algorithm method is used to estimate the parameter of the model. …”
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Reproducing kernel Hilbert space method for cox proportional hazard model by Abdul Manaf, Nur'azah

“…This algorithm is used to determine the vector i a that enables us to find the optimal parameters of ƒ(x)which is simplified as F(x)= ∑aᵢK(x,xᵢ) . …”
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Application of remote sensing techniques for prediction of landslide hazard areas in Malaysia by Ahmad, Samsudin, Seeni Mohd., Mohd. Ibrahim, Hashim, Mazlan

“…From these data, simple algorithm were used to classify the area into different risk zones. …”
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Statistical process control for failure crushing time data using competing risks model. by Elfaki, F.A.M., Daud, Isa, Ibrahim, Noor Akma, Daud, J., Azram, M., Usman, M.

“…EM algorithm method is used to estimate the parameter of the model. …”
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Demand analysis of flood insurance by using logistic regression model and genetic algorithm by Sidi, P., Mamat, M.B., Sukono, ., Supian, S., Putra, A.S.

“…The analysis was done by using logistic regression model, and to estimate model parameters, it is done with genetic algorithm. …”
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Competing risks for reliability analysis using Cox’s model by Mohamed Elfaki, Faiz Ahmed, Daud, Isa, Ibrahim, Nor Azowa, Abdullah, M. Y., Usman, Mustofa

“…This paper seeks to show that, with a large sample size based on expectation maximization (EM) algorithm, both models give similar results. Design/methodology/approach – The parameters of the models have been estimated by method of maximum likelihood based on EM algorithm. …”
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Modeling financial environments using geometric fractional Brownian motion model with long memory stochastic volatility by Al Haqyan, Mohammed Kamel Mohammed

“…Therefore, this research develops a new GFBM model that can better describe and reflect real life situations particularly in financial scenario. All parameters involved in the developed model are estimated by using innovation algorithm. …”
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Application of the generalized likelihood uncertainty estimation (GLUE) approach for assessing uncertainty in hydrological models: A review by Mirzaei, M., Huang, Y.F., El-Shafie, A., Shatirah, A.

“…In this article, we present an overview of the application of GLUE for assessing uncertainty distribution in hydrological models particularly surface and subsurface hydrology and briefly describe algorithms for sampling of the prior parameter in hydrologic simulation models.…”
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Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review by Diandra Chika Fransisca, Sukono, Diah Chaerani, Nurfadhlina Abdul Halim

Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis by Mohamed Elfaki, Faiz Ahmed

“…The Expectation Maximization (EM) algorithm is utilized to obtain the estimate of the parameters in the models. …”
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Modelling and Forecasting the Kuala Lumpur Composite Index Rate of Returns Using Generalised Autoregressive Conditional Heteroscedasticity Models by Abdul Muthalib, Maiyastri

“…Methods for correcting the outliers and splitting the heterogeneous data are proposed. The EM algorithm is applied to split the heterogeneous data, and the estimated parameters are used to correct the outlying data using the Mahalanobis Distance. …”
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Predictive analytic dashboard for desalter and crude distillation unit by Hassanudin, S.N., Aziz, I.A., Jaafar, J., Qaiyum, S., Zubir, W.M.A.M.

“…Artificial Neural Network (ANN) algorithm is used with R programming language for the forecasting. …”
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Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region by Bhattacharya S., Ali T., Chakravortti S., Pal T., Majee B.K., Mondal A., Pande C.B., Bilal M., Rahman M.T., Chakrabortty R.

“…This study employs various machine learning and deep learning algorithms, specifically Random Forest (RF), Artificial Neural Network (ANN), and Deep Learning Neural Network (DLNN), to estimate landslide susceptibility in Chamoli district, Uttarakhand, India?…”

Groundwater quality assessment and optimization of monitored wells using multivariate geostatistical techniques in Amol-Babol Plain, Iran by Narany, Tahoora Sheikhy

“…A new optimization approach was proposed for redesign monitoring network wells using optimization algorithm based on the vulnerability of aquifer to contaminations, estimation error of sampling wells, nearest distance between wells, and source of contamination in the study area. …”
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