Most approaches to estimate the fundamental matrix assume a Gaussian distribution in the errors in view of mathematical tractability. However, this assumption is violated if the distribution computed is not normal. In this paper we propose a robust approach of estimating the fundamental matrix which does not rely on the Gaussian assumption. The proposed technique, weighted least squares (WLS), is the application of linear mixed-effects models considering the correlation between different data sub-samples. It provides an unbiased estimation of the fundamental matrix which is not affected by outlier samples. Experimental results on synthetic and real images confirm the accuracy of our method and its superiority to standard estimation methods.