In this thesis two methodologies have been proposed for evaluating the fault response of analog/RF circuits. These proposed approaches are used to evaluate the response of the faulty circuit in terms of specifications/measurements. Faulty response can be used to evaluate important test metrics like fail probability, fault coverage and yield coverage of given measurements under process variations. Once the models for faulty and fault free circuit are generated, one needs to perform Monte Carlo sampling (as opposed to Monte Carlo simulations) to compute these statistical parameters with high accuracy. The first method is based on adaptively determining the order of the model based on the error budget in terms of computing the statistical metrics and position of the threshold(s) to decide how precisely necessary models need to be extracted. In the second method, using hierarchy in process variations a hybrid of heuristics and localized linear models have been proposed. Experiments on LNA and Mixer using the adaptive model order selection procedure can reduce the number of necessary simulations by 7.54x and 7.03x respectively in the computation of fail probability for an error budget of 2%. Experiments on LNA using the hybrid approach can reduce the number of necessary simulations by 21.9x and 17x for four and six output parameters cases for improved accuracy in test statistics estimation.