Statistics, and statistical inference in specific, are becoming an ever greater part of our daily lives. Models are trying to estimate anything from (future) consumer behaviour to optimal steering behaviours and we need these models to be as accurate as possible. Trevor Hastie is a great contributor to the development of the field, and I highly recommend the machine learning books and courses that he developed, together with Robert Tibshirani. These you may find in my list of R Resources (Cheatsheets, Tutorials, & Books).

Today I wanted to share another book Hastie wrote, together with Bradley Efron, another colleague of his at Stanford University. It is called Computer Age Statistical Inference (Efron & Hastie, 2016) and is a definite must read for every aspiring data scientist because it illustrates most algorithms commonly used in modern-day statistical inference. Many of these algorithms Hastie and his colleagues at Stanford developed themselves and the book handles among others:

• Regression:
  • Logistic regression
  • Poisson regression
  • Ridge regression
  • Jackknife regression
  • Least angle regression
  • Lasso regression
  • Regression trees
• Bootstrapping
• Boosting
• Cross-validation
• Random forests
• Survival analysis
• Support vector machines
• Kernel smoothing
• Neural networks
• Deep learning
• Bayesian statistics

 

 

• best practices
• learning
• machine learning
• statistics