Noisy data

  Noise comes in two distinct flavors. First is erratic bursty noise which is difficult to fit into a statistical model. It bursts out of our simple models. To handle this noise we need ``robust'' estimation procedures which we consider first.

Next is noise that has a characteristic spectrum, temporal spectrum, spatial spectrum, or dip spectrum. Low frequency drift of the mean value of a signal is often called secular noise.

In real life, we need to handle both bursty noise and secular noise at the same time.