Autoregressive methods applied to real-valued but noisy multicomponent exponential data can generate large numbers of parameters. Complex-valued components modeling oscillatory behavior often appear among these parameters. These appear as complex-conjugate pairs to ensure a net real-valued contribution, and they typically model noise or artifacts of imperfect measurements, such as oscillations in the first several spin-echoes sometimes observed in transverse relaxation measurements. Component selection heuristics are therefore needed to determine the number of components to extract, and to evaluate if the components are physically meaningful. For example, oscillatory components can be excluded because they model noise or non-exponential components. Components having decay rates that are fast relative to the decay sampling rate can often be excluded because they model noise in the first few points. Depending on circumstances, components decaying (or growing) much more slowly than the total acquisition time may be rejected as unphysical, but they also can model baseline offsets or equilibrium levels towards which magnetization relaxes.