Neural network for censored expectile regression based on data augmentation

Key Points

• This method enhances the accuracy of censored regression outcomes through data augmentation techniques, and leads to more reliable predictions.
• The neural network demonstrated improved performance metrics compared to traditional regression models in handling censored data variables.
• Data augmentation is crucial in addressing the challenges posed by missing data, enabling the model to learn more effectively from limited datasets.
• This approach highlights the potential for artificial intelligence in advancing statistical methods in various fields, requiring further exploration.