Proving that FFT (and by extension any correlation based estimator) is unbiased
A had a long email discussion about the topic of bias in frequency formulas, aka estimators with a really sharp Grad student a while back.  It makes me hesitant to speak authoritatively on statistical matters.
I don't like the term estimator as it is too broad.
Formulas come in two basic flavors, either they are exact or they are not.  Exact means mathematically exact.  So, if you feed it the DFT of a noiseless pure tone (real or complex, different formulas for each), the calculation will result in an answer limited only by the precision of the calculation.
Other formulas are approximations.  Approximations are often times cheaper to calculate and good enough for the job.  In heavier noise, the precision advantage of a more precise formula may be rendered useless, even a disadvantage.  Approximation formulas are inherently biased.  Run a set of tones through them and maybe, like a broken clock, it will appear unbiased for certain tones.
Now run add your run-of-the-mill AWGN to the pure tone signal and test the frequency formulas.  Does the Gaussian shape pass through unskewed and still centered?
The (excellent, Thanks Julien) paper I cited in the comment didn't take quite that approach.
I would be quite interested in another, up to date, comparison with emphasis on CRLB calculation (especially on the real tone case) of all my formulas against the known standards (Start with the cited list, I think he missed Macleods.)
Here is another similar page:
http://www.ericjacobsen.org/fe2/fe2.htm
If you do endeavor to do a comparitive analysis, and there no better way to learn the alternatives, and post it somewhere, please cite it in a comment on this answer.
Frequency on bin (which seems to be what you are asking about in terms of a native bin) cases are sort of a different beast than non-whole cycle per frame tones.
The technique cited in my articles can be extended to any arbitrary number of bins.