Deep Neural Network Estimator

Deep neural network (DNN) estimators are increasingly used for statistical inference and prediction tasks, aiming to improve accuracy and efficiency compared to traditional methods. Current research focuses on developing robust DNN architectures and algorithms, including sparse-penalized regularization and adversarial training, to handle various data types (e.g., dependent, high-dimensional) and loss functions. These advancements enable applications in diverse fields, such as performance modeling of AI accelerators, time series prediction, and causal inference (e.g., estimating average treatment effects), offering improved accuracy and scalability for complex problems. The development of reliable confidence intervals and efficient subsampling techniques further enhances the practical utility of DNN estimators for statistical inference.