"nonparametric" is somewhat of a (confusing!) misnomer in that it doesn't mean no parameters, but lots of them where the # of parameters grows with # of instances [1]. In all of these cases the models have some general parameter(s) as well.
Some simple examples would be the bin-width and bandwidth in the histogram and the kernel density estimator. A somewhat complex example would be Dirichlet Process-based Mixture Models [2]; this has a "concentration" parameter. The terminology is used outside of density estimation too, e.g., Support Vector Machines (SVM) and k-Nearest Neighbors are considered nonparametric [3].