This book presents recent developments in multivariate and robust statistical methods. Featuring contributions by leading experts in the field it covers various topics, including multivariate and high-dimensional methods, time series, graphical models, robust estimation, supervised learning and normal extremes. It will appeal to statistics and data science researchers, PhD students and practitioners who are interested in modern multivariate and robust statistics. The book is dedicated to David E. Tyler on the occasion of his pending retirement and also includes a review contribution on the popular Tyler’s shape matrix.

Global inflation has surged to 7.5 percent in August 2022, from an average of 2.1 percent in the decade preceding the COVID-19 pandemic, threatening to become an entrenched phenomenon. This paper disentangles the confluence of contributing factors to the post-pandemic rise in consumer price inflation, using monthly data and a battery of econometric methodologies covering a panel of 30 European countries over the period 2002-2022. We find that while global factors continue to shape inflation dynamics throughout Europe, country-specific factors, including monetary and fiscal policy responses to the crisis, have also gained greater prominence in determining consumer price inflation during the pandemic period. Coupled with increasing persistence in inflation, these structural shifts call for significant and an extended period of monetary tightening and fiscal realignment.

Factor models have become the most successful tool in the analysis and forecasting of high-dimensional time series. This monograph provides an extensive account of the so-called General Dynamic Factor Model methods. The topics covered include: asymptotic representation problems, estimation, forecasting, identification of the number of factors, identification of structural shocks, volatility analysis, and applications to macroeconomic and financial data.

Covering some of the key areas of optimal control theory (OCT), a rapidly expanding field, the authors use new methods to set out a version of OCT’s more refined ‘maximum principle.’ The results obtained have applications in production planning, reinsurance-dividend management, multi-model sliding mode control, and multi-model differential games. This book explores material that will be of great interest to post-graduate students, researchers, and practitioners in applied mathematics and engineering, particularly in the area of systems and control.

Control and Dynamic Systems: Advances in Theory and Application, Volume 51: Robust Control System Techniques and Applications Part 2 of 2 discusses system robustness techniques. This volume presents a comprehensive treatment of robust system techniques in nonlinear, linear, and multilinear interval systems. It also covers techniques for dealing with system disturbances, system modeling approximations, and parameter uncertainties. This volume ends by reviewing robustness techniques for systems with structured state space uncertainty. This volume will be of great use as a reference source for mechanical and electrical engineers.