**Problem of the Week**

**Math Club**

**BUGCAT**

**Zassenhaus Conference**

**Hilton Memorial Lecture**

**BingAWM**

seminars:stat:160218

Statistics Seminar

Department of Mathematical Sciences

DATE: | Thursday, February 18, 2016 |
---|---|

TIME: | 1:15pm to 2:15pm |

LOCATION: | WH 100E |

SPEAKER: | Anton Schick, Binghamton University |

TITLE: | Convergence rates of kernel density estimators in the $L_1$ norm |

**Abstract**

The usual approach to evaluate the performance of a kernel density estimator (KDE) is to look at the mean integrated square error. This provides rates of convergence in the $L_2$-norm. In this talk rates of convergence in the $L_1$-norm are presented. We consider both estimators of a density $f$ and its convolution $f*f$ with itself. In the former case the rates are nonparametric $n^{-s/(2s+1)}$ and depend on the smoothness $s$ of $f$. In the second case we obtain the parametric rate $n^{-1/2}$.

seminars/stat/160218.txt · Last modified: 2016/05/01 21:47 by aleksey

Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Noncommercial-Share Alike 3.0 Unported