Article

Title: Data-Driven Bandwidth Selection for Recursive Kernel Density Estimators Under Double Truncation

Author(s): Debashis Paul, Jung Won Hyun and Prabir Burman
Issue: Volume 80 Series B Part 2 Year 2018
Pages: 341 -- 368
Abstract
In this paper we proposed a data-driven bandwidth selection procedure of the recursive kernel density estimators under double truncation. We showed that, using the selected bandwidth and a special stepsize, the proposed recursive estimators outperform the nonrecursive one in terms of estimation error in many situations. We corroborated these theoretical results through simulation study. The proposed estimators are then applied to data on the luminosity of quasars in astronomy. We corroborated these theoretical results through simulation study, then, we applied the proposed estimators to data on the luminosity of quasars in astronomy.
AMS (2000) subject classification. Primary 62G07, 62L20; Secondary 65D10, 62N01.
Keywords and phrases: Density estimation, Stochastic approximation algorithm, Smoothing, Curve fitting, Double truncated data.