Statistical Inference via Convex Optimization

Discover the groundbreaking insights in "Statistical Inference via Convex Optimization," authored by Anatoli Juditsky and Arkadi Nemirovski. Published by Princeton University Press in 2020, this hardback edition spans an impressive 656 pages. This authoritative text delves into the intersection of high-dimensional statistics and optimization, presenting a comprehensive analysis of key issues such as hypothesis testing and signal recovery.

Through a clear and engaging approach, Juditsky and Nemirovski illustrate how the principles of convex optimization can be effectively utilized to develop and evaluate near-optimal statistical solutions. This book is an essential resource for researchers and professionals looking to enhance their understanding of statistical inference methods and their practical applications within the realm of optimization.

Ideal for both academic and professional settings, "Statistical Inference via Convex Optimization" is a must-have addition to any statistician's library. Experience the fusion of theory and application in this meticulously crafted volume.