| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1152110 | Statistics & Probability Letters | 2012 | 9 Pages |
Abstract
In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is proposed. This method is natural when estimating an unknown density function of a positive random variable. The rates of Mean Squared Error, Mean Integrated Squared Error, and the L1L1-consistency are investigated. Simulation studies are conducted to compare a new estimator and its modified version with traditional kernel density construction.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Robert Mnatsakanov, Khachatur Sarkisian,