Regressions in Covariances, Dependencies and Graphs

Mohsen Pourahmadi and Aramayis Dallakyan

This book addresses the analysis of multivariate data with cross-sectional and temporal dependencies through the lens of regression, modeling covariances, copulas, and graphs. It presents parsimonious models for classical data and regularization methods for high-dimensional data, with special emphasis on graphical Lasso algorithms. Each chapter concludes with practical, ready-to-run R scripts that bring concepts to life. The companion R package recode provides curated datasets and functions to implement and visualize the methods discussed. Whether you're a student, researcher, or data scientist, this book bridges theory and practice, equipping you with tools to apply advanced statistical methods to real-world problems.