Table 1: Performance of the different parameter estimators. The given uncertainties is the standard deviation of the fit parameters obtained from different artificial datasets.

Estimator		 $\xi _{\rm R}$ $\xi _{\rm M}$ $b_{\rm X}$

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 $1.09 \pm 0.08$ $-0.46 \pm 0.09$ $4.0 \pm 2.3$


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