\begin{table}%t1
%\centering
\par
\caption{Performance of the different parameter estimators. The given uncertainties is the standard deviation of the fit parameters obtained from different artificial datasets.\label{estimators}}
\begin{tabular}{lccc}
\hline \hline
\noalign{\smallskip}
Estimator &  $\xi_{\rm R}$ & $\xi_{\rm M}$ & $b_{\rm X}$ \\
\hline \noalign{\smallskip}
Assumed parameters & 1.40 & --0.85 & --4.9 \\
Merit function & $1.42 \pm 0.13$ & $-0.87 \pm 0.16$ & $-5.4 \pm 3.6$ \\
Merit function with exact knowledge of scatter & $1.40\pm 0.08$ & $-0.85\pm 0.09$ & $-4.8\pm 2.3$ \\ 
Merit function with isotropic uncertainties & $1.54 \pm 0.16$ & $-1.03 \pm 0.19$ & $-8.5 \pm 4.4$ \\
Maximum likelihood & $1.10 \pm 0.08$ & $-0.47 \pm 0.09$ & $3.5 \pm 2.1$\\
Maximum likelihood with exact knowledge of scatter &  $1.25 \pm 0.06$ & $-0.68 \pm 0.07$ & $-0.51 \pm 1.68$ \\
Ordinary least squares &  $1.09 \pm 0.08$ & $-0.46 \pm 0.09$ & $4.0 \pm 2.3$ \\
\hline
\end{tabular}
\end{table}