Editing Allan variance (section)

===Non-overlapped variable τ estimators===
Taking the time-series and skipping past ''n''&nbsp;−&nbsp;1 samples, a new (shorter) time-series would occur with ''τ''<sub>0</sub> as the time between the adjacent samples, for which the Allan variance could be calculated with the simple estimators. These could be modified to introduce the new variable ''n'' such that no new time-series would have to be generated, but rather the original time series could be reused for various values of ''n''. The estimators become

:<math>\sigma_y^2(n\tau_0, M) = \operatorname{AVAR}(n\tau_0, M) = \frac{1}{2\frac{M-1}{n}} \sum_{i=0}^{\frac{M-1}{n} - 1}\left(\bar{y}_{ni+n} - \bar{y}_{ni}\right)^2</math>

with <math>n \le \frac{M - 1}{2}</math>,

and for the time series:

:<math>\sigma_y^2(n\tau_0, N) = \operatorname{AVAR}(n\tau_0, N) = \frac{1}{2n^2\tau_0^2\left(\frac{N - 1}{n} - 1\right)} \sum_{i=0}^{\frac{N-1}{n} - 2}\left(x_{ni+2n} - 2x_{ni+n} + x_{ni}\right)^2</math>

with <math>n \le \frac{N - 1}{2}</math>.

These estimators have a significant drawback in that they will drop a significant amount of sample data, as only 1/''n'' of the available samples is being used.