Mathematical Statistics with Applications in R, Third Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods, such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem-solving in a logical manner. Step-by-step procedure to solve real problems make the topics very accessible.

• Presents step-by-step procedures to solve real problems, making each topic more accessible
• Provides updated application exercises in each chapter, blending theory and modern methods with the use of R
• Includes new chapters on Categorical Data Analysis and Extreme Value Theory with Applications
• Wide array coverage of ANOVA, Nonparametric, Bayesian and empirical methods