This book treats the latest developments in the theory of order-restricted inference, with
special attention to nonparametric methods and algorithmic aspects. Among the topics …

A framework is presented for generalized additive modelling under shape constraints on the
component functions of the linear predictor of the GAM. We represent shape constrained …

Splines are important tools for the flexible modeling of curves and surfaces in regression
analyses. Functions for constructing spline basis functions are available in R through the …

Multi-omic studies combine measurements at different molecular levels to build
comprehensive models of cellular systems. The success of a multi-omic data analysis …

We consider the problem of nonparametric regression under shape constraints. The main
examples include isotonic regression (with respect to any partial order), unimodal/convex …

We propose a new, nonparametric method for multivariate regression subject to convexity or
concavity constraints on the response function. Convexity constraints are common in …

The objective of this article is to develop a computationally efficient estimator of the
regression function subject to various shape constraints. In particular, nonparametric …

Big data features not only large volumes of data but also data with complicated structures.
Complexity imposes unique challenges on big data analytics. Meeker and Hong (2014; …

In light of Earth's changing climate and growing human population, there is an urgent need
to improve monitoring of natural and anthropogenic disturbances which effect forests' ability …

This article reviews recent advances in estimation and inference for nonparametric and
semiparametric models with endogeneity. It first describes methods of sieves and …