Ery Arias-Castro

Principles of Statistical Analysis Principles of Statistical Analysis (eBook, PDF)

Learning from Randomized Experiments

• Format: PDF

Produktbeschreibung

This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance - simulation and sampling, as well as experimental design and data collection - that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 30. April 2022
• Englisch
• ISBN-13: 9781108805285
• Artikelnr.: 66180402

Autorenporträt

Ery Arias-Castro is a professor in the Department of Mathematics and in the Hal¿c¿ölu Data Science Institute at the University of California, San Diego, where he specializes in theoretical statistics and machine learning. His education includes a bachelor's degree in mathematics and a master's degree in artificial intelligence, both from École Normale Supérieure de Cachan (now École Normale Supérieure Paris-Saclay) in France, as well as a Ph.D. in statistics from Stanford University in the United States.

Inhaltsangabe

Preface
Acknowledgments
Part I. Elements of Probability Theory: 1. Axioms of probability theory
2. Discrete probability spaces
3. Distributions on the real line
4. Discrete distributions
5. Continuous distributions
6. Multivariate distributions
7. Expectation and concentration
8. Convergence of random variables
9. Stochastic processes
Part II. Practical Considerations: 10. Sampling and simulation
11. Data collection
Part III. Elements of Statistical Inference: 12. Models, estimators, and tests
13. Properties of estimators and tests
14. One proportion
15. Multiple proportions
16. One numerical sample
17. Multiple numerical samples
18. Multiple paired numerical samples
19. Correlation analysis
20. Multiple testing
21. Regression analysis
22. Foundational issues
References
Index.