Deep-Learning-Assisted Statistical Methods with Examples in R - Tianyu Zhan

This book explores how deep learning enhances statistical methods for hypothesis testing, point estimation, optimization, interpretation, and other aspects. It uniquely demonstrates leveraging deep learning to improve traditional statistical approaches, showcasing their superior performance in practical applications. Each topic includes essential background, clear method explanations, and detailed R code demonstrations through case studies. This allows readers to directly apply these methods to their own challenges and easily adapt the underlying principles to related problems. This book delves into statistical inference, introducing advanced strategies for hypothesis testing and point estimation. These innovative methods ingeniously combine both artificial and human intelligence, offering robust solutions for scenarios where traditional optimal analytical solutions are elusive or non-existent. A prime example of their real-world impact is in adaptive clinical trials, where these computational approaches can be readily implemented to optimize trial design and outcomes. The author further explores the multifaceted benefits of deep-learning-assisted statistical methods, extending beyond mere statistical efficiency. It highlights crucial features such as integrity protection, ensuring the trustworthiness of results; computational efficiency, enabling faster and more scalable analyses; and interpretability, which is increasingly vital for transparent communication of complex fi
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