Time Series Analysis and Its Applications, presents a comprehensive treatment of both time and frequency domain methods with accompanying theory. Extensive examples illustrate solutions to climate change, monitoring a nuclear test ban treaty, evaluating the volatility of an asset, and more.
This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
As the size of data sets grows ever larger, the need for valid statistical tools is greater than ever. This book introduces super learning and the targeted maximum likelihood estimator, and discusses complex data structures and related applied topics.
This valuable compendium of statistical methods features a unique combination of methodology, theory, algorithms and applications. It covers recently developed approaches to handling large and complex data sets, including the Lasso and boosting methods.
Statistics and Data Analysis for Financial Engineering provides an overview of the methods and techniques used to extract quantitative information from enormous amounts of data. The text includes R Labs with real-data exercises, and integrates graphical and analytical methods for modeling and diagnosing modeling errors.
This example-based general introduction to the statistical computing environment does not assume any previous familiarity with R or other software packages. R functions are compellingly presented in the context of interesting applications with real data.
This accessible book provides a versatile treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It contains many worked out examples and exercises.
This updated textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models.
The theory of inequalities has applications in virtually every branch of mathematics. This revised and expanded edition of a classic work on inequalities will be of interest to statisticians, probabilists, and mathematicians.