Statistical Learning for Big Dependent Data

This book offers advanced methods for analyzing large, dynamically dependent datasets, providing practical tools and real-world applications throughout.

This insightful resource provides a comprehensive presentation of statistical and machine learning methods essential for analyzing and forecasting large, dynamically dependent datasets. Statistical Learning for Big Dependent Data begins with visualization tools, enabling readers to grasp the complexity of big dependent data. It discusses procedures for identifying outliers, clusters, and various forms of heterogeneity, which are crucial for effective data analysis.

The book delves into dimension reduction techniques such as regularization and factor models, including the use of regularized Lasso in the context of dynamical dependence. It also introduces a range of forecasting procedures, from index models to boosting and now-casting. In addition, Statistical Learning for Big Dependent Data covers advanced machine-learning methods, including neural networks, deep learning, and various tree-based approaches.

To enhance practical understanding, the text provides real-world examples and utilizes numerous R packages. It also includes an associated R package that allows readers to replicate the analyses presented and apply them in real-world scenarios. This book is an invaluable resource for PhD students and researchers in fields such as business, economics, engineering, and science, as well as practitioners seeking to deepen their knowledge of statistical and machine learning methods for big dependent data analysis.