Estimation of a k-monotone density: characterizations, consistency and minimax lower bounds.

Abstract

The classes of monotone or convex (and necessarily monotone) densities on ℝ(+) can be viewed as special cases of the classes of k-monotone densities on ℝ(+). These classes bridge the gap between the classes of monotone (1-monotone) and convex decreasing (2-monotone) densities for which asymptotic results are known, and the class of completely monotone (∞-monotone) densities on ℝ(+). In this paper we consider non-parametric maximum likelihood and least squares estimators of a k-monotone density g(0).We prove existence of the estimators and give characterizations. We also establish consistency properties, and show that the estimators are splines of degree k - 1 with simple knots. We further provide asymptotic minimax risk lower bounds for estimating the derivatives[Formula: see text], at a fixed point x(0) under the assumption that [Formula: see text].