Statistical Modeling and Computation

This book, Statistical Modeling and Computation, provides a unique introduction to modern statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of mathematical statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications.

The 2nd edition changes the programming language used in the text from MATLAB to Julia. For all examples with computing components, the authors provide data sets and their own Julia codes. The new edition features numerous full color graphics to illustrate the concepts discussed in the text, and adds three entirely new chapters on a variety of popular topics, including:

• Regularization and the Lasso regression
• Bayesian shrinkage methods
• Nonparametric statistical tests
• Splines and the Gaussian process regression

Table of Contents

Part I: Fundamentals of Probability

1. Probability Models  

2. Random Variables and Probability Distributions  

3. Joint Distributions  

Part II: Statistical Modeling and Frequentist and Bayesian Inference

4. Common Statistical Models  

5. Statistical Inference  

6. Likelihood  

7. Monte Carlo Sampling  

8. Bayesian Inference  

Part III: Advanced Models and Inference

9. Shrinkage and Regularization  

10. Generalized Linear Models  

11. Nonparametric Methods  

12. Dependent Data Models  

13. State Space Models  

About the Author

Joshua C. C. Chan is Professor of Economics, and holds the endowed Olson Chair at Purdue University. He is an elected fellow at the International Association for Applied Econometrics and served as Chair for the Economics, Finance and Business Section of the International Society for Bayesian Analysis from 2020-2022. His research focuses on building new high-dimensional time-series models and developing efficient estimation methods for these models. He has published over 50 papers in peer-reviewed journals, including some top-field journals such as Journal of Econometrics, Journal of the American Statistical Association and Journal of Business and Economic Statistics.

Dirk Kroese is Professor of Mathematics and Statistics at the University of Queensland. He is known for his significant contributions to the fields of applied probability, mathematical statistics, machine learning, and Monte Carlo methods. He has published over 140 articles and 7 books. He is a pioneer of the well-known Cross-Entropy (CE) method, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance. In addition to his scholarly contributions, Dirk Kroese is recognized for his role as an educator and mentor, having supervised and inspired numerous students and researchers.