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--- seminars:sml:160426 [2016/04/21 02:09] – qiao
+++ seminars:sml:160426 [2016/04/21 02:11] (current) – qiao
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+<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
+<WRAP center box 80%>
+<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.
+</WRAP>