Deep-Learning-Assisted Statistical Methods with Examples in R
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Deep-Learning-Assisted Statistical Methods with Examples in R
Zhan, Tianyu
Taylor & Francis Ltd
03/2026
156
Mole
Inglês
9781041158431
Pré-lançamento - envio 15 a 20 dias após a sua edição
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I Introduction and Preparations
1. Introduction to Deep Neural Networks (DNNs)
2. How to Implement DNN in Regression
II Statistical Inference
3. Two-sample Parametric Hypothesis Testing
4. Point Estimation
III Numerical Methods
5. Optimization with Unavailable Gradient Information
6. Protect Integrity and Save Computational Time
7. Interpretable Models in Regression Analysis
IV Extensions
8. Substitutions of Other Methods for DNN
9. Limitations and Mitigations
10. Some Future Works
1. Introduction to Deep Neural Networks (DNNs)
2. How to Implement DNN in Regression
II Statistical Inference
3. Two-sample Parametric Hypothesis Testing
4. Point Estimation
III Numerical Methods
5. Optimization with Unavailable Gradient Information
6. Protect Integrity and Save Computational Time
7. Interpretable Models in Regression Analysis
IV Extensions
8. Substitutions of Other Methods for DNN
9. Limitations and Mitigations
10. Some Future Works
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
statistical inference;adaptive clinical trials;interpretable models;computational statistics;integrity protection;scientific data analysis;deep learning for statistical inference
I Introduction and Preparations
1. Introduction to Deep Neural Networks (DNNs)
2. How to Implement DNN in Regression
II Statistical Inference
3. Two-sample Parametric Hypothesis Testing
4. Point Estimation
III Numerical Methods
5. Optimization with Unavailable Gradient Information
6. Protect Integrity and Save Computational Time
7. Interpretable Models in Regression Analysis
IV Extensions
8. Substitutions of Other Methods for DNN
9. Limitations and Mitigations
10. Some Future Works
1. Introduction to Deep Neural Networks (DNNs)
2. How to Implement DNN in Regression
II Statistical Inference
3. Two-sample Parametric Hypothesis Testing
4. Point Estimation
III Numerical Methods
5. Optimization with Unavailable Gradient Information
6. Protect Integrity and Save Computational Time
7. Interpretable Models in Regression Analysis
IV Extensions
8. Substitutions of Other Methods for DNN
9. Limitations and Mitigations
10. Some Future Works
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
