# 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.**