# Seminar at SMU Delhi

March 25, 2015 (Wednesday) , 3:30 PM at Webinar
Motivated by both the shortcomings of high-order density estimators, and the increasingly large datasets in many areas of modern science, in this talk, we introduce new high-order, nonparametric density estimators that are guaranteed to be positive and do not have highly oscillatory tails. Our approach is based on data perturbation, for example by tilting or data sharpening. It leads to new estimators that are more accurate than conventional kernel techniques that use positive kernels, but which nevertheless enjoy the positivity property, and are far less wiggly'' than high-order kernel estimators. We investigate performance by theoretical~analysis and in a numerical study. [Joint work with Peter Hall: To appear in JRSS-B].