A list of recommended books to learn more about Statistics, the majority are freely available from the Broome Library:


  • Introduction to Statistics and Data Analysis

    • Heumann and Shalabh
  • Statistical Foundations, Reasoning and Inference

    • Kauermann, Küchenhoff, and Heumann


  • 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


  • Semiparametric Regression with R

    • Harezlak, Ruppert, and Wand

    • Graduate


  • 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


  • 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


  • 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


  • 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.