content_image/300046.jpg content_image/300045.jpg content_image/300044.jpg content_image/300043.jpg
for ψ ∈ G is 

and consider e$_{nh}$ = 1 n ∑ n j $_{=1}$e$_{nhj}$ ; this generalized function provides ̂ f − f. The expectation functional Ee$_{hn}$ 

gives the generalized bias of the estima- tor ˆ f, Bias ( ˆ f ) , see (8) . 

<img src='content_image/300046.jpg'>

Next to derive the variance functional consider T$_{lj}$ = E ( e$_{nhl}$, ψ$_{1}$ )( e$_{hnj}$, ψ$_{2}$ )) . For l ̸ = j by independence 

For l = j 

where 

<img src='content_image/300045.jpg'>

<img src='content_image/300044.jpg'>

<img src='content_image/300043.jpg'>

