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Offers a brief, well-organised, and easy-to-follow introduction and overview of robust statistics. Huber focuses primarily on the important and clearly understood case of distribution robustness, where the shape of the true underlying distribution deviates slightly from the assumed model (usually the Gaussian law).

A new edition of the classic, groundbreaking book on robust statistics Over twenty-five years after the publication of its predecessor, Robust Statistics, Second Edition continues to provide an authoritative and systematic treatment of the topic. This new edition has been thoroughly updated and expanded to reflect the latest advances in the field while also outlining the established theory and applications for building a solid foundation in r...

A comprehensive overview of statistical data analysis research, featuring real-world case studies and applications How should data analysis be taught? How valid are the results? How should one deal with inhomogeneous data? What kinds of computing languages should be used, if used at all? These are but a few of the many challenging questions surrounding the fundamentals of data analysis. Data Analysis: What Can Be Learned from the Past 50 Year...