NONPARAMETRIC ESTIMATION OF MULTIVARIATE CONVEX-TRANSFORMED DENSITIES.

ABSTRACT: We study estimation of multivariate densities p of the form p(x) = h(g(x)) for x ∈ ℝ(d) and for a fixed monotone function h and an unknown convex function g. The canonical example is h(y) = e(-y) for y ∈ ℝ; in this case, the resulting class of densities [Formula: see text]is well known as the class of log-concave densities. Other functions h allow for classes of densities with heavier tails than the log-concave class.We first investigate when the maximum likelihood estimator p̂ exists for the class P(h) for various choices of monotone transformations h, including decreasing and increasing functions h. The resulting models for increasing transformations h extend the classes of log-convex densities studied previously in the econometrics literature, corresponding to h(y) = exp(y).We then esta

SUBMITTER: Seregin A 

PROVIDER: S-EPMC3058562 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature