Nonparametric Estimation under Shape Constraints
Estimators, Algorithms and Asymptotics

'I recommend this impressive book very enthusiastically to both young and senior researchers interested in shape-restricted nonparametric estimation. Closing an important gap in the literature, it contains not only classical material on nonparametric estimation of monotone functions in a series of application fields but also an introduction to advanced themes that are the topic of active ongoing research - in particular, estimation of convex functions, interval censoring, higher dimensional models, and other complex models in order-restricted inference. Interesting and enjoyable, the book clearly motivates models and methods by illustrative data examples and intuitive heuristic explanations of the necessary asymptotic mathematical theory, accompanied by clear and detailed proofs of the theory.' Enno Mammen, Institute of Applied Mathematics, Heidelberg University

'A comprehensive study of the state of the art in nonparametric shape-restricted inference by two experts in the field. A clear-cut cogent presentation style, along with a careful exposition of the mathematics as well as the algorithmic aspects of the optimization problems involved, makes this a very well-rounded text that should prove an asset to both mathematically trained scientists seeking a rigorous exposure to the field and statistical researchers interested in the 'current status' of affairs in shape-restricted inference.' Moulinath Banerjee, University of Michigan, Ann Arbor

'The book provides an up-to-date comprehensive review of both classical and new methods for shape constrained estimators. It does so in a clear and well-explained manner, including many real-world examples to motivate the methodology and theory. As such it contains a nice mix of theory and applications, and so should be of interest to both students and researchers. … I thoroughly enjoyed reading this book: it gives a detailed treatment of most relevant features of shape constrained estimation, and does so in a manner that makes it immensely readable, whether you are a novice or an expert in the area.' Dennis Kristensen, MathSciNet Mathematical Reviews (www.ams.org/mr-database)