= Conditional quantile identification
{title2=$M(\theta)>M(\theta_0)\quad(\theta\ne\theta_0)$}
If the conditional error <distribution function> is continuous and strictly increasing with $\tau$-<quantile> zero, the conditional expected <check loss> is uniquely minimized at zero fitted displacement. If every incorrect parameter differs from the true regression function on an event of positive probability, averaging these nonnegative conditional risk differences gives strict <identifiability> of the true parameter. Integrable errors and bounded regression functions supply finite risks.
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