[@pee80], and is related to the convolution of the two-point correlation function in redshift space, $\xi^{\rm (s)}({{\mbox{\boldmath $s$}}},{{\mbox{\boldmath $s$}}}')$. Including the shot noise term, the relation is $$R_{ij} = \int d^3s d^3s' K_i({{\mbox{\boldmath $s$}}}) K_j({{\mbox{\boldmath $s$}}}')
   n({{\mbox{\boldmath $s$}}}) n({{\mbox{\boldmath $s$}}}') \xi^{\rm (s)}({{\mbox{\boldmath $s$}}},{{\mbox{\boldmath $s$}}}') +
   \int d^3s K_i({{\mbox{\boldmath $s$}}}) K_j({{\mbox{\boldmath $s$}}}) n({{\mbox{\boldmath $s$}}}) + E_{ij},
\label{eq2-8}$$ where the first term corresponds to the signal, second term corresponds to the shot noise, and the last term, $E_{ij}$, is the correlation matrix for other sources of noise such as magnitude errors or uncertainty in the luminosity function, which is assumed to be independent from the clustering signal for simplicity. One can reasonably assume that the selection function $n({{\mbox{\boldmath $s$}}})$ within each cell is approximately constant and can be replaced by $n_i = N_i/V_i$, in which case the equation (\[eq2-8\]) reduces to $$R_{ij} = N_i N_j \xi^{\rm (s)}_{ij} +
   \sqrt{N_i N_j} K_{ij} + E_{ij},
\label{eq2-9}$$ (no sum over $i$, $j$) where $$\xi^{\rm (s)}_{ij} =
   \frac{1}{V_i V_j} 
   \int d^3s d^3s'
   K_i({{\mbox{\boldmath $s$}}}) K_j({{\mbox{\boldmath $s$}}}')
   \xi^{\rm (s)}\left({{\mbox{\boldmath $s$}}}, {{\mbox{\boldmath $s$}}}'\right),
\label{eq2-9-1}$$ and $$K_{ij} =
   \frac{1}{\sqrt{V_i V_j}} 
   \int d^3s
   K_i({{\mbox{\boldmath $s$}}}) K_j({{\mbox{\boldmath $s$}}}).
\label{eq2-9-2}$$ The first term in equation (\[eq2-9\]) contains the model parameters, and corresponds 