Efficient performance modeling is an extremely important task for yield analysis and design optimization of analog circuits. In this paper, a novel regression modeling method based on hierarchical shrinkage priors is proposed to construct hybrid performance models with both high accuracy and low computational cost. In particular, the user-defined model templates derived from design equations and the general-purpose orthogonal polynomials are combined together to set up a hybrid dictionary. Next, in order to avoid over-shrinking large model coefficients, a novel regression method based on hierarchical shrinkage priors and variational Bayesian inference is adopted for model fitting. A rail-to-rail operational amplifier example demonstrates that the proposed method achieves up to 40% error reduction over other state-of-the-art approaches without increasing the modeling cost.