Asymptotically best bandwidth selectors in kernel density estimation

This paper gives asymptotically best data based choices of the bandwidth of the kernel density estimator. These bandwidth selectors attain the fastest possible rate of convergence to the desired theoretical optimum and the best possible constant coefficient in the spirit of the usual Fisher Information, with the use of only nonnegative kernel estimators at all stages of the selection process.