SUMMARY
This discussion focuses on recommendations for mathematically rigorous texts on statistics and probability. Key suggestions include "Data Analysis - A Bayesian Tutorial" for a practical introduction, and several books by Lehmann, specifically "Theory of Point Estimation" and "Testing Statistical Hypotheses," which are frequently referenced for their rigor. Other notable mentions include "Mathematical Statistics" and "Statistical Inference" as well as a classic text from 60 years ago. Participants emphasize the importance of reviewing materials before purchase to ensure they meet individual learning needs.
PREREQUISITES
- Understanding of linear and abstract algebra
- Knowledge of calculus and analysis
- Familiarity with Bayesian statistics
- Ability to interpret mathematical proofs
NEXT STEPS
- Research "Data Analysis - A Bayesian Tutorial" for foundational knowledge
- Explore Lehmann's "Theory of Point Estimation" for rigorous statistical theory
- Investigate "Statistical Inference" for comprehensive coverage of statistical methods
- Review classic texts in statistics to understand historical context and development
USEFUL FOR
Students, researchers, and professionals in mathematics, statistics, and data science seeking a deep understanding of statistical theory and its applications.