Fig. 3

Illustrating how a signal value, the black point, is estimated using a kernel with an iteratively increasing smoothing parameter, h. Increasing from h ₀ to h ₁, i.e., a higher degree of smoothing, the variance in the estimated value can be explained by the variance in the original signal. However, when the smoothing parameter is increased from h ₁ to h ₂, the kernel reaches the step in the signal and the estimate at the black point can no longer be explained by the signal variance