Regressions in Covariances, Dependencies and Graphs

Regressions in Covariances, Dependencies and Graphs, Volume 1, provides a comprehensive exploration of advanced statistical modeling for multivariate data. This mathematics and probability and statistics text focuses on the critical role of regression in modeling cross-sectional, temporal, and spatial dependencies. By applying principles of parsimony and regularization, the book examines covariance regression, hidden regression, and graphical Lasso algorithms. These methods allow researchers to model complex covariance matrices and sparse graphs within high-dimensional data environments.

The volume covers essential topics such as dimension reduction through principal component analysis and the application of factor models to business and economics data. To bridge the gap between theoretical statistics and practical application, Mohsen Pourahmadi includes genuine datasets and ready-to-run R scripts in every chapter. Users can also utilize the companion R package, recode, to implement and visualize these sophisticated statistical methodologies in real-world scenarios.