As someone who’s already delved into advanced topics like Casella and Berger, grad-level stochastic analysis, and convex optimization, you’re probably looking for something that takes risk analysis to the next level. But are there books or fields that go deep into calculating risk?
The answer lies in a combination of advanced statistical techniques, machine learning, and domain-specific knowledge. Risk analysis is often application-based, and the tools and methods used can vary greatly depending on the context.
For instance, in finance, risk analysis might involve advanced statistical models like GARCH or stochastic volatility models. In engineering, it might involve probabilistic risk assessment or reliability engineering. And in economics, it might involve econometric modeling or decision theory.
Some advanced books that might be of interest include:
* **Risk Analysis: A Quantitative Guide** by David Vose, which provides a comprehensive overview of risk analysis techniques and their applications.
* **Probability and Risk Analysis: An Introduction for Engineers** by Jim Henley, which focuses on the application of probability theory to risk analysis in engineering.
* **Decision Analysis: Introductory Readings** by Howard Raiffa, which explores the application of decision theory to risk analysis in various fields.
Ultimately, the key to advanced risk analysis is to combine domain-specific knowledge with advanced statistical and machine learning techniques. By doing so, you can develop a deeper understanding of risk and uncertainty, and make more informed decisions in your field of expertise.
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*Further reading: [Risk Analysis Tutorial](https://www.stat.tamu.edu/~jones/stat661/lecture-notes/node34.html)*