Fitting of a positron emission tomography (PET) time-activity curve is typically accomplished according to the least squares (LS) criterion, which is optimal for data having Gaussian distributed errors, but not robust in the presence of outliers. Conversely, quantile regression (QR) provides robust estimates not heavily influenced by outliers, sacrificing a little efficiency relative to LS when no outliers are present. Given these considerations, we hypothesized that QR would improve parameter estimate accuracy as measured by reduced intersubject variance in distribution volume (V(T)) compared with LS in PET modeling. We compare V(T) values after applying QR with those using LS on 49 controls studied with [(11)C]-WAY-100635. QR decreases the standard deviation of the V(T) estimates (relative improvement range: 0.08% to 3.24%), while keeping the within-group average V(T) values almost unchanged. QR variance reduction results in fewer subjects required to maintain the same statistical power in group analysis without additional hardware and/or image registration to correct head motion.