  Oscillator fluctuations are described as the phase or frequency noise
spectrum, or in terms of a wavelet variance as a function of the measurement
time. The spectrum is generally approximated by the `power law,' i.e., a
Laurent polynomial with integer exponents of the frequency. This article
extends the domain of application of PVAR, a wavelet variance which uses the
linear regression on phase data to estimate the frequency, and called
`parabolic' because such regression is equivalent to a parabolic-shaped weight
function applied to frequency fluctuations. In turn, PVAR is relevant in that
it improves on the widely-used Modified Allan variance (MVAR) enabling the
detection of the same noise processes at the same confidence level in a shorter
measurement time. More specifically, we provide (i) the analytical expression
of the response of the PVAR to the frequency-noise spectrum in the general case
of non-integer exponents of the frequency, and (ii) a useful approximate
expression of the statistical uncertainty.
