Nonparametric Estimation under Shape Constraints: Estimators, Algorithms and Asymptotics

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Convex-constrained Sparse Additive Modeling and Its Extensions (2017, background): “Meaningful and precise rates of convergence have been obtained if the regression function m is smooth, or, as we see more recently [GJ14], if m satisﬁes certain shape restrictions (e.g., linear, monotonic, convex, or log-concave).”
Handbook of Big Data (2016, background): “The reader is referred to [40] for a recent book on the subject.”
Current status linear regression (2016, unknown): “The theoretical justification of this approach can be proved using the asymptotic theory of smooth functionals given in [11], page 286.”