pandas (original) (raw)

ewmvar() and ewmcov() contain the following code:

    if not bias:
        result *= (1.0 + 2.0 * com) / (2.0 * com)

Where does this scaling formula come from? Does the number of observations not enter in to the bias of the (sample) variance estimation? I would imagine that perhaps the formula above is for the case that have an infinite sample, but for a finite sample I would have thought that the number of observations would enter into things.