I had to read a lot of books in graduate school. Some were life-changing, and others were forgettable.
If I could bring a reading list back in time for my ‘first year’ graduate self, it would include the following:
Third Edition, by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin
Probably the most useful book I’ve ever owned.
by Norman Matloff
This book made me less bad at programming in R.
Second Edition, by Judea Pearl
Ecology is complicated. We often lack replicated controlled experiments with random treatment assignment. This book helps with that.
by Noel Cressie and Christopher Wikle
A thoughtful treatment of hierarchical modeling in a spatial, temporal, and spatiotemporal context. Has breadth with a healthy dose of outside references for depth.
by Benjamin M. Bolker
Covers fundamental ideas about likelihood and process-oriented modeling while building R proficiency.
by N. Thompson Hobbs & Mevin B. Hooten
An introduction to the process of model building and estimation for non-math/stats oriented readers.
by Andrew Gelman and Jennifer Hill
A gentle introduction to multilevel modeling, with plenty of graphics and integration with R.
Second Edition, by George Casella and Roger L. Berger
Essential for understanding the mathematical and probabilistic foundations of statistics. Read it after brushing up on calculus.
by George Shilov
I wish I had taken a class in linear algebra as an undergraduate, but I instead had to catch up in my first year of grad school. This book made it relatively painless.
by David Guichard and friends
Because I took a few calculus classes in high school and college and didn’t know why.
by Odo Diekmann, Hans Heesterbeek & Tom Britton
Mathematical epidemiology is a huge topic. This book introduces common models and approaches from first principles, with plenty of problems along the way to make sure you’re following along. Read it with a notebook and pencil handy.