The remedian uses a $k\times b$ matrix to approximate the median of $n\leq b^{k}$ streaming input values by recursively replacing buffers of $b$ values with their medians, thereby ignoring its $200(\lceil b/2\rceil / b)^{k}%$ most extreme inputs. Rousseeuw & Bassett (1990) and Chao & Lin (1993); Chen & Chen (2005) study the remedian's distribution as $k\rightarrow\infty$ and as $k,b\rightarrow\infty$. The remedian's breakdown point vanishes as $k\rightarrow\infty$, but approaches $(1/2)^{k}$ as $b\rightarrow\infty$. We study the remedian's robust-regime distribution as $b\rightarrow\infty$, deriving a normal distribution for standardized (mean, median, remedian, remedian rank) as $b\rightarrow\infty$, thereby illuminating the remedian's accuracy in approximating the sample median. We derive the asymptotic efficiency of the remedian relative to the mean and the median. Finally, we discuss the estimation of more than one quantile at once, proposing an asymptotic distribution for the random vector that results when we apply remedian estimation in parallel to the components of i.i.d. random vectors.