If the problem you are talking about is related to encountering non-constant variance in your model, I would recommend weighted least square approaches.
In regression analysis it is important for the sample sizes to be the same for the dependent and the independent variables. That is, a sample size of n is given by
(yi, x1i, x2i, ... , xmi), I = 1, 2, 3, ..., n
for the variables Y, X1, X2, ... , Xm. Do you mean that the sample sizes of the variables could be unequal?
You might try weighting each data group by the inverse of its sample variance (1/s^2). Google: weight "inverse of the sample variance" for more information.