DENSITY ESTIMATION BY WAVELET-BASED REPRODUCING KERNELS
Su‐Yun Huang · 1999
Density estimation by wavelet-based reproducing kernels is studied. Asymptotic bias and variance are derived. Estimators using spline-wavelets and Daubechies wavelets are presented as examples. Kernel order and kernel efficiency are also discussed. By an integral property of the bias and an idea from Scott's averaged shifted histograms, a bias reduction technique based on a grid point average is proposed. This bias reduction technique is shown to be variance stable.