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.