Abstract
Least-squares cross-validation is commonly used for selection of smoothing parameters in the discrete data setting; however, in many applied situations, it tends to select relatively small bandwidths. This tendency to undersmooth is due in part to the geometric weighting scheme that many discrete kernels possess. This problem may be avoided by using alternative kernel functions. Specifically, discrete versions (both unordered and ordered) of the popular Epanechnikov kernel do not have rapidly decaying weights. The analytic properties of these kernels are contrasted with commonly used discrete kernel functions and their relative performance is compared using both simulated and real data. The simulation and empirical results show that these kernel functions generally perform well and in some cases demonstrate substantial gains in terms of mean squared error.
