I have two sets of real-valued data and I am interested in their correlation. From my perspective, there appear to be errors both variables, so I am inclined to perform a regression with TLS (Total Least Squares). Other analysts (perhaps because of lack of comfort with error-in-variables regression) prefer OLS.
I would like to compare the likelihoods of the two models, or use some principled model selection criterion. However, it occurs to me that if the TLS regressed slope is non-zero, then the residual variance of the TLS solution will be strictly less than the OLS solution. If so, it seems like TLS will always "win". (I guess that last statement assumes that both models have equal prior probability.)
Is there a good way to compare these two models?