Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5129836 | Statistics & Probability Letters | 2017 | 6 Pages |
Abstract
We define a new bandwidth-dependent kernel density estimator that improves existing convergence rates for the bias, and preserves that of the variation, when the error is measured in L1. No additional assumptions are imposed to the extant literature.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Kairat Mynbaev, Carlos Martins-Filho,