A nonparametric technique for modeling the behavior of power amplifiers (PAs) is presented. The proposed technique relies on the principles of density estimation using the kernel method and is suited for use in PA modeling. The proposed methodology transforms the input domain into an orthogonal memory domain. In this domain, nonparametric static functions are discovered using the kernel estimator. These orthogonal nonparametric functions can be fitted with any desired mathematical structure, thus facilitating its implementation. Furthermore, due to orthogonality, the nonparametric functions can be analyzed and discarded individually, which simplifies pruning basis functions and provides a tradeoff between complexity and performance. The results show that the methodology can be employed to model PAs, therein yielding error performance similar to state-of-the-art parametric models. Furthermore, a parameter-efficient model structure with six coefficients was derived for a Doherty PA, therein significantly reducing the deployment's computational complexity. Finally, the methodology can also be well exploited in digital linearization techniques.