Constrain PD values using a logistic transformation

Dear NMusers, I am trying to model some PD data, which has a lower bound of zero and an upper bound of 100. I was wondering how to implement this restriction and if it was possible to use the general logistic transformation in the $ERROR block shown below: $ERROR IPRE=A(1) LT=LOG(IPRE/(100-IPRE))+ERR(1) Y=(100*EXP(LT))/(1+EXP(LT)) If this is appropriate, do I understand correctly that this is NOT a transform both sides approach; i.e. DV stays in its original or natural form. Finally, the logistic transformation extends from -∞ to +∞. However, the dataset does have a small number of values that are zeros and 100 (Five zeros and a couple of 100s in a dataset of about 700 observations). Do these small number of extreme values in the dataset cause problem when the LT term is back transformed above. Any other method and references for papers that use these types of constraints would be greatly appreciated. Warm regards and thanks in advance...MNS