\Re^c$, possibly with a sequence of covariate vectors ${{}x}_1, \ldots, {{}x}_k$, with ${{}x}_i = (x_{i1}, \ldots, x_{iq_i})^\top \in \mathcal{X} \subset
\Re^{q_i}$. Any two distinct random vectors ${{}Y}_i$ and ${{}Y}_j$ may share random scalar components. We denote the distinct scalar random variables by $Z_1, \ldots, Z_m$, and by $G(z_1, \ldots, z_m)$ their typically unknown, underlying joint distribution function. In general $m \le \sum_{i = 1}^k c_i$, with equality only if ${{}Y}_1, \ldots, {{}Y}_k$ have distinct components. Let ${{}Y} = ({{}Y}_1^\top, \ldots, {{}Y}_k^\top)^\top$, and denote by $X$ the set of ${{}x}_1, \ldots, {{}x}_k$.

On of the common aims in statistical modelling is to estimate at least a sub-vector of an unknown parameter vector ${{}\theta} \in \Theta \subset \Re^p$ using data ${{}y}_1, \ldots, {{}y}_k$ and ${{}x}_1, \ldots, {{}x}_k$. This is most commonly achieved through a vector of $p$ estimating functions $\sum_{i = 1}^k{{}\psi}^i({{}\theta}) = (\sum_{i =
  1}^k\psi_1^i({{}\theta}), \ldots, \sum_{i = 1}^k\psi_{p}^i({{}\theta}))^\top$, where ${{}\psi}^i({{}\theta}) = {{}\psi}({{}\theta}, {{}Y}_i, {{}x}_i)$ and $\psi^i_r({{}\theta}) = \psi_r({{}\theta}, {{}Y}_i, {{}x}_i)$, $(r = 1,\ldots,p)$. In particular, ${{}\theta}$ is estimated by the $M$-estimator $\hat{{}\theta}$ [@vaart:1998 Chapter 5], which results by the solution of the system of estimating equations $$\label{estimating_eqautions}
  \sum_{i = 1}^k{{}\psi}^i({{}\theta}) = {{}0}_p \, ,$$ with respect to ${{}\theta}$, where ${{}0}_p$ 