Books
A list of recommended books to learn more about Statistics, the majority are freely available from the Broome Library:
Basics
Introduction to Statistics and Data Analysis
- Heumann and Shalabh
Statistical Foundations, Reasoning and Inference
- Kauermann, Küchenhoff, and Heumann
Regression
Generalized Linear Models With Examples in R
Dunn and Smyth
Graduate
Linear and Generalized Linear Mixed Models and Their Applications (2nd Edition)
Jiang and Nguyen
Graudate
Regression Modeling Strategies
Harrell
Undergraduate
Vector Generalized Linear and Additive Models
Yee
Graduate
Nonparametric
Semiparametric Regression with R
Harezlak, Ruppert, and Wand
Graduate
Computational
Bootstrap Methods with applications in R
Dikta and Scheer
Graduate
Modern Optimization with R (2nd Edition)
Cortez
Graduate
Computational Statistics
- Gentle
Monte Carlo and Quasi-Monte Carlo Sampling
- Lemieux
Statistics With Julia
- Nazarathy andKlok
Introducing Monte Carlo Methods in R
- Robert and Casella
Permutation Statistical Methods with R
- Berry, Kvamme, Johnston, and Mielke
Monte Carlo Strategies in Scientific Computing
- Liu
Bayesian
Introduction to Bayesian Inference, Methods and Computation
- Heard
Applied Bayesian Statistics
- Cowles
Bayesian Statistical Modeling with Stan, R, and Python
- Matsuura
Bayesian Essentials in R
- Marin and Robert
Theoretical
Essentials of Stochastic Processes (3rd Edition)
Durrett
Graduate
A Concise Introduction to Measure Theory
Shirali
Graduate
Large Sample Techniques for Statistics (2nd Edition)
Jiang
Graduate
A Course in Mathematical Statistics and Large Sample Theory
Bhattacharya, Lin, and Patrangenaru
Graduate
Mixture and Hidden Markov Models with R
Visser and Speekenbrink
Undergraduate
Modern Mathematical Statistics (3rd Edition)
Devore, Berk, and Carlton
Undergraduate
Probability Theory (3rd Edition)
Klenke
Graduate
Testing Statistical Hypotheses (4th Edition)
Lehmann and Romano
Graduate
Theory of Point Estimation
Lehmann and Casella
Graduate
May not be available
Longitudinal Data Analysis
Longitudinal Categorical Data Analysis
- Sutradhar
Survival Analysis
Statistical Modelling of Survival Data with Random Effects
- Ha, Jeong, and Lee
Survival Analysis (3rd Edition)
- Kleinbaum and Klein
Applied Survival Analysis in R
- Moore
Bayesian Survival Analysis
- Ibrahim, Chen, and Sinha
Survival Analysis Techniques for Censored and Truncated Data (2nd Edition)
- Klein and Moeschberger
Machine Learning
Fundamental of High-Dimensional Statistics
- Lederer
An Introduction to Statistical Learning (2nd Edition)
- James, Witten, Hastie and Tibshirani
Statistical Learning from a Regression Perspective (2nd Edition)
- Berk
Elements of Statistical Learning
- Hastie, Friedman, and Tibshirani
Statistics for High Dimensional Data
- Bühlmann and van der Geer
Probability and Statistics for Machine Learning
- Das Gupta
Time-Series
Introduction to Time Series and Forcasting (3rd Edition)
- Brockwell and Davis
Time Series Analysis and Its Applications
- Shumway and Stoffer
Time Series Analysis for the State-Space Model with R/Stan
- Hagiwara
Study Desing and Causal Inference
Causal Inference What IF
- Hernán and Robins
Design of Observational Studies
- Rosenbaum
Bolded Titles, I have read thoroughly.