
<WRAP centeralign>##Statistical Machine Learning Seminar##\\ Hosted by Department of Mathematical Sciences</WRAP>

~~META:title=April 26, 2016~~
  * 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

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<WRAP centeralign>**//Abstract//**</WRAP>
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.
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