Deep-Learning-Assisted Statistical Methods...

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Sprache: Englisch

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Beschreibung

This book explores how deep learning enhances statistical methods for hypothesis testing, point estimation, optimization, interpretation, and other aspects. This book is a valuable resource for students, practitioners, and researchers integrating statistics and data science techniques to solve impactful real-world problems.

Über den Autor

Tianyu Zhan is a Director at AbbVie Inc. He earned his Ph.D. in Biostatistics from the University of Michigan Ann Arbor in 2017. His research interests are closely related to late-phase clinical trials. He has been actively promoting innovative clinical trial designs and advanced analysis methods at AbbVie, resulting in significant business impacts.

Inhaltsverzeichnis

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

Details

Erscheinungsjahr:	2026
Fachbereich:	Wahrscheinlichkeitstheorie
Genre:	Importe , Mathematik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	Einband - flex.(Paperback)
ISBN-13:	9781041158431
ISBN-10:	1041158432
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Zhan, Tianyu
Hersteller:	Chapman and Hall/CRC
Verantwortliche Person für die EU:	Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße:	234 x 156 x 10 mm
Von/Mit:	Tianyu Zhan
Erscheinungsdatum:	17.03.2026
Gewicht:	0,289 kg