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Full-Text Articles in Physical Sciences and Mathematics
Regularized Neural Network To Identify Potential Breast Cancer: A Bayesian Approach, Hansapani S. Rodrigo, Chris P. Tsokos, Taysseer Sharaf
Regularized Neural Network To Identify Potential Breast Cancer: A Bayesian Approach, Hansapani S. Rodrigo, Chris P. Tsokos, Taysseer Sharaf
Journal of Modern Applied Statistical Methods
In the current study, we have exemplified the use of Bayesian neural networks for breast cancer classification using the evidence procedure. The optimal Bayesian network has 81% overall accuracy in correctly classifying the true status of breast cancer patients, 59% sensitivity in correctly detecting the malignancy and 83% specificity in correctly detecting the non-malignancy. The area under the receiver operating characteristic curve (0.7940) shows that this is a moderate classification model.
A Comparison Of Estimation Methods For Nonlinear Mixed-Effects Models Under Model Misspecification And Data Sparseness: A Simulation Study, Jeffrey R. Harring, Junhui Liu
A Comparison Of Estimation Methods For Nonlinear Mixed-Effects Models Under Model Misspecification And Data Sparseness: A Simulation Study, Jeffrey R. Harring, Junhui Liu
Journal of Modern Applied Statistical Methods
A Monte Carlo simulation is employed to investigate the performance of five estimation methods of nonlinear mixed effects models in terms of parameter recovery and efficiency of both regression coefficients and variance/covariance parameters under varying levels of data sparseness and model misspecification.