Summary: In the double kernel density estimate, the smoothing parameter \(h\) is chosen so as to minimize the \(L_ 1\) distance between two kernel density estimates having identical smoothing factors but different kernels. This method is known to be consistent for any density and to be asymptotically optimal for a certain smooth class of densities. We propose a plug-in modification of the estimate and introduce various other data-based bandwidth estimates. Finally, a simulation study is presented in which the new bandwidth selectors are compared with a host of well-known methods.