Series: Chapman & Hall/CRC Texts in Statistical Science

Design of Experiments

Bayes, AI, and Deep Learning

Bayes, AI, and Deep Learning

Bayesian Ideas and Data Analysis

Time Series

A Course in Regression and Smoothing Methods

The Analysis of Time Series

Markov Chain Monte Carlo

Experimental Design for Data Science and Engineering

Theory of Spatial Statistics

Time Series

Nonparametric Inference

Applied Statistics

The Analysis of Time Series

Foundations of Bayesian Statistics for Data Scientists

Introduction to Modern Randomization-Based Design and Analysis for Causal Inference

Statistical Inference

Interpreting Data

A Course in Large-Sample and High-Dimensional Theory

Foundations of Bayesian Statistics for Data Scientists

Linear Models with R

Bayesian Statistical Methods

Survival Analysis

Introduction to Statistical Process Control

Regressions in Covariances, Dependencies and Graphs

Markov Chain Monte Carlo

Statistical Modeling and Data Analysis

Applied Stochastic Modelling

Spatio-Temporal Methods in Environmental Epidemiology with R

Bayes Rules!

The Analysis of Time Series

Sampling

Theory of Spatial Statistics

A Primer on Linear Models

Elementary Applications of Probability Theory

Elements of Simulation

Analysis of Failure and Survival Data

Stochastic Processes

Generalized Linear Mixed Models

Richly Parameterized Linear Models

Introduction to the Theory of Statistical Inference

Logistic Regression Models

Statistical Analysis of Financial Data

Linear Models with Python

The Bugs Book

A Course in Categorical Data Analysis

Analysis of Failure and Survival Data

Logistic Regression Models

Principles of Uncertainty

An Introduction to Statistical Inference and Its Applications with R