convolution equations. 5. The nonparametric regression model with classical measurement error and an additional observation. This type of model was examined by Li (2002) and Li and Hsiao (2004); the convolution equations derived here provide a convenient representation. Often models of this type were considered in semiparametric settings. Bu- tucea and Taupin (2008) (extending the earlier approach by Taupin, 2001) consider a regression function known up to a finite dimensional parameter with the mismeasured variable observed with independent error where the error distribution is known. Under the latter condition the model 5 here would reduce to the two first equations where f$_{u}$ is known and two unknown functions are g (here nonparametric) and f$_{x}$ ∗ . The model 5 incorporates model 3 for the regressor and thus the convolu- tion equations from that model apply. An additional convolution equation is derived here; it is obtained from considering the value of the density weighted conditional expectation in the dual space of generalized functions, S $^{∗}$, applied to arbitrary ψ ∈ S, 24 ( w, ψ ) = ( E ( f ( z ) y | z ) , ψ ) = ( E ( f ( z ) g ( x $^{∗}$) | z ) , ψ ); f$_{x}$ ∗ ∗ f$_{u}$ = f$_{z}$ ; ( gf$_{x}$ ∗ ) ∗ f$_{u}$ = w,