content_image/300056.jpg
<img src='content_image/300056.jpg'>

Notes. Notation: k = 1 , 2 ...d ; in model 5. w = E ( yf$_{z}$ ( z ) | z ); w$_{k}$ = E ( x$_{k}$f$_{z}$ ( z ) | z ); in model 6. w = E ( y | z ); in model 7. w = E ( y | z ); w$_{k}$ = E ( x$_{k}$y | z ) . 

Theorem 2. Under Assumption 1 for each of the models 5-7 the corre- sponding convolution equations hold. 

The proof is in the derivations of the next subsection. 

## 2.3.2 Discussion of the regression models and derivation of the 

