A Bayesian approach to non‐parametric monotone function estimation

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Monotonic Calibrated Interpolated Look-Up Tables (2015, methodology): “For example, building on Ramsay (1998), Shively et al. (2009) parameterize the set of all smooth and strictly monotonic one-dimensional functions using an integrated exponential form f ( x ) = a + x 0 e b + u ( t ) dt , and showed better performance than the monotone functions estimators of Neelon…”
Modeling recovery curves with application to prostatectomy. (2015, methodology): “post-treatment trajectory, which includes the initial post-treatment value. Furthermore, they do not place an upper bound on the asymptotic level of the predicted function. Isotonic regression models [25, 14, 17, 3], enforce the predicted functions to be monotonic, but do not naturally output recove”
Multivariate convex regression with adaptive partitioning (2011, background): “The latter has been studied extensively with many approaches; for examples, see Brunk (1955), Hall & Huang (2001), Neelon & Dunson (2004) and Shively et al. (2009).”