Deep-Learning-Assisted Statistical Methods with Examples in R
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 pract...
Saved in:
| Main Author: | |
|---|---|
| Format: | Online |
| Language: | English |
| Published: |
CRC Press
2026
|
| Subjects: | |
| Online Access: | https://library.oapen.org/handle/20.500.12657/110290 |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1869531533756334080 |
|---|---|
| author | Zhan, Tianyu |
| author_browse | Zhan, Tianyu |
| author_facet | Zhan, Tianyu |
| author_sort | Zhan, Tianyu |
| collection | Directory of Open Access Books |
| description | 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 findings in modern statistics. This section encourages readers to consider a broader spectrum of improvements for new statistical methods, focusing on attributes that enhance their practical utility and societal relevance. Finally, the reader is given a critical examination of the limitations and potential concerns associated with the methods presented in earlier chapters. Crucially, it doesn't just identify these issues but also offers constructive mitigation approaches. This equips readers with essential techniques to safeguard AI-based methodologies with their scientific expertise, ensuring responsible and valid application of these powerful computational tools in diverse scientific and practical domains. This book is a valuable resource for students, practitioners, and researchers integrating statistics and data science techniques to solve impactful real-world problems. |
| format | Online |
| id | doab-20.500.12854ir-173288 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | CRC Press |
| publisherStr | CRC Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1732882026-03-01T08:40:45Z Deep-Learning-Assisted Statistical Methods with Examples in R Zhan, Tianyu Statistical inference Adaptive clinical trials Interpretable models Computational statistics Integrity protection Scientific data analysis Deep learning for statistical inference thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics::PBTB Bayesian inference thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQN Neural networks and fuzzy systems 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 findings in modern statistics. This section encourages readers to consider a broader spectrum of improvements for new statistical methods, focusing on attributes that enhance their practical utility and societal relevance. Finally, the reader is given a critical examination of the limitations and potential concerns associated with the methods presented in earlier chapters. Crucially, it doesn't just identify these issues but also offers constructive mitigation approaches. This equips readers with essential techniques to safeguard AI-based methodologies with their scientific expertise, ensuring responsible and valid application of these powerful computational tools in diverse scientific and practical domains. This book is a valuable resource for students, practitioners, and researchers integrating statistics and data science techniques to solve impactful real-world problems. 2026-03-01T08:40:41Z 2026-03-01T08:40:41Z 2026-02-28T20:20:48Z 2026 book https://library.oapen.org/handle/20.500.12657/110290 9781040600719 9781003681489 9781040600764 https://directory.doabooks.org/handle/20.500.12854/173288 eng Chapman & Hall/CRC Data Science Series open access CRC Press Chapman and Hall/CRC 10.1201/9781003681489 10.1201/9781003681489 82beefe5-482e-4277-9971-e4ee0480a152 9781040600719 9781003681489 9781040600764 Chapman and Hall/CRC 184 open access |
| spellingShingle | Statistical inference Adaptive clinical trials Interpretable models Computational statistics Integrity protection Scientific data analysis Deep learning for statistical inference thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics::PBTB Bayesian inference thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQN Neural networks and fuzzy systems Zhan, Tianyu Deep-Learning-Assisted Statistical Methods with Examples in R |
| title | Deep-Learning-Assisted Statistical Methods with Examples in R |
| title_full | Deep-Learning-Assisted Statistical Methods with Examples in R |
| title_fullStr | Deep-Learning-Assisted Statistical Methods with Examples in R |
| title_full_unstemmed | Deep-Learning-Assisted Statistical Methods with Examples in R |
| title_short | Deep-Learning-Assisted Statistical Methods with Examples in R |
| title_sort | deep learning assisted statistical methods with examples in r |
| topic | Statistical inference Adaptive clinical trials Interpretable models Computational statistics Integrity protection Scientific data analysis Deep learning for statistical inference thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics::PBTB Bayesian inference thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQN Neural networks and fuzzy systems |
| topic_facet | Statistical inference Adaptive clinical trials Interpretable models Computational statistics Integrity protection Scientific data analysis Deep learning for statistical inference thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics::PBTB Bayesian inference thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQN Neural networks and fuzzy systems |
| url | https://library.oapen.org/handle/20.500.12657/110290 |
| work_keys_str_mv | AT zhantianyu deeplearningassistedstatisticalmethodswithexamplesinr |