  A wavelet-based changepoint method is proposed that determines when the
variability of the noise in a sequence of functional profiles goes
out-of-control from a known, fixed value. The functional portion of the
profiles are allowed to come from a large class of functions and may vary from
profile to profile. The proposed method makes use of the orthogonal properties
of wavelet projections to accurately and efficiently monitor the level of noise
from one profile to the next. Several alternative implementations of the
estimator are compared on a variety of conditions, including allowing the
wavelet noise subspace to be substantially contaminated by the profile's
functional structure. The proposed method is shown to be very efficient at
detecting when the variability has changed through an extensive simulation
study.
