Kernel Density Estimation

The most classical nonparametric density estimation method, which places a kernel function, typically a symmetric probability density such as a Gaussian kernel, at each data point and sums and normalizes all kernel functions to obtain a density estimate. The convergence rate is determined by the bandwidth parameter, attaining the optimal rate of n^{-s/(2s+1)} for densities with smoothness s.