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    Statistical modeling via bootstrapping and weighted techniques based on variances by Wan Ahmad, Wan Muhamad Amir, Aleng, Nor Azlida, Ali, Z, Mohd Ibrahim, Mohamad Shafiq

    Published 2018
    “…This data will be applied to the multiple logistic regression algorithm and modified Bayesian logistic regression. …”
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    Article
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    Comparing three methods of handling multicollinearity using simulation approach by Adnan, Norliza

    Published 2006
    “…In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory variables. …”
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    Thesis
  5. 5

    A Comparative Study On Some Methods For Handling Multicollinearity Problems by Adnan, Norliza, Ahmad, Maizah Hura, Adnan, Robiah

    Published 2006
    “…In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory variables. …”
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    Article
  6. 6

    A comparative study on some methods for handling multicollinearity problems by Adnan, Norliza, Ahmad, Maizah Hura, Adnan, Robiah

    Published 2006
    “…In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory variables. …”
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    Article
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    Comparison between fuzzy bootstrap weighted multiple linear regression and multiple linear regression: a case study for oral cancer modelling by Mohd Ibrahim, Mohamad Shafiq, Wan Ahmad, Wan Muhamad Amir, Hasan, Ruhaya, Harun, Masitah Hayati

    Published 2018
    “…Three different SAS algorithms (i) bootstrap multiple linear regression (BMLR), (ii) bootstrap weighted Bayesian multiple linear regression (BWBMLR), and (iii) fuzzy bootstrap weighted multiple linear regression (FBWMLR) were compared separately according to their average width of prediction. …”
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    Proceeding Paper
  9. 9

    Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models by Quadros, Jaimon Dennis, Khan, Sher Afghan, Aabid, Abdul, Baig, Muneer

    Published 2023
    “…The data for training and testing the algorithms was derived using the regression equation developed using the Box-Behnken Design (BBD). …”
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    Article
  10. 10

    Identification of suitable explanatory variable in goldfeld-quandt test and robust inference under heteroscedasticity and high leverage points by Muhammadu, Adamu Adamu

    Published 2016
    “…This study has developed an algorithm of identifying this variable prior to conducting the Goldfeld-Quandt test in multiple linear regression model. …”
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    Thesis
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    Robust Estimation Methods And Outlier Detection In Mediation Models by Fitrianto, Anwar

    Published 2010
    “…Mediation models refer to the relationships among three variables: an independent variables (IV), a potential mediating variable (M), and a dependent variable (DV). …”
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    Thesis
  12. 12

    Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

    Published 2017
    “…The project has also implemented the optimization process onto the proposed ANFIS model through the hybrid of Genetic Algorithm on the fuzzy membership function of the ANFIS model. …”
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    Thesis
  13. 13

    Predicting motorcycle customization preferences using machine learning by Saputra, Ananta, Utoro, Rio Korio, Roedavan, Rickman, Soegiarto, Duddy, Moorthy, Kohbalan, Pratondo, Agus

    Published 2025
    “…The classification model was developed using the Random Forest algorithm, Support Vector Machine and Logistic Regression with 5-fold Cross validation. …”
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    Conference or Workshop Item
  14. 14

    Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

    Published 2018
    “…And the few existing ones that can work for regression tasks were recently found to underestimate mutual information between two strongly dependent variables. …”
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    Thesis
  15. 15

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

    Published 2004
    “…The Expectation Maximization (EM) algorithm is utilized to obtain the estimate of the parameters in the models. …”
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    Thesis