**Problem of the Week**

**Math Club**

**BUGCAT 2018**

**DST and GT Day**

**Number Theory Conf.**

**Zassenhaus Conference**

**Hilton Memorial Lecture**

seminars:sml:160426

Statistical Machine Learning Seminar

Hosted by Department of Mathematical Sciences

- Date: Tuesday, April 26, 2016
- Time: 12:00-1:00
- Room: WH-100E
- Speaker: Wolfgang Wefelmeyer (Universität zu Köln)
- Title: Density estimators in regression models with errors in covariates

*Abstract*

In regression models $Y=r(X)+\varepsilon$ with $X$ and $\varepsilon$ independent, the density of the response $Y$ can be estimated by a convolution of (kernel) estimators for the densities of $r(X)$ and $\varepsilon$. The rate of this convolution estimator depends on the smoothness of the densities of $X$ and $\varepsilon$ and on the smoothness and local flatness of the regression function $r$. When we observe the covariates $X$ with measurement errors, $Z=X+\eta$, we need deconvolution estimators for the densities of $X$ and $\varepsilon$ and for $r$. This is joint work with Anton Schick and Ursula U. Müller.

seminars/sml/160426.txt · Last modified: 2016/04/20 22:11 by qiao

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