Higher order kernels for density and distribution estimation
Author Godisemo
4 Stars
Updated Last
1 Year Ago
Started In
February 2018


This package provides basic kernel density estimation using higher order kernels also known as bias reducing kernels. At the moment only kernels from the polynomial family described in [1] and the Gaussian family described in [2]. This includes some widely used kernels:

  • Uniform
  • Epanechnikov
  • Biweight
  • Triweight
  • Gaussian

and higher order versions. Kernel code is generated automatically, so any order is possible.

For bandwidht selection, Silverman's rule-of-thumb is implemented for all kernels.


using HigherOrderKernels
using Plots
using Distributions

data = randn(10000)
xgrid = linspace(-2, 2, 100)
plt = plot()
for ν = 2:2:8
  k = EpanechnikovKernel{ν}
  h = bandwidth(k, data)
  p = kpdf.(k, xgrid, [data], h)
  plot!(plt, xgrid, p, label="Order ")
plot!(x -> pdf(Normal(), x), label="Exact")


  1. Hansen, B. E. (2005). EXACT MEAN INTEGRATED SQUARED ERROR OF HIGHER ORDER KERNEL ESTIMATORS. Econometric Theory, 21(6), 1031–1057. http://doi.org/10.1017/S0266466605050528
  2. Wand, M. P., & Schucany, W. R. (1990). Gaussian-based kernels. The Canadian Journal of Statistics. La Revue Canadienne De Statistique, 18(3), 197–204. http://doi.org/10.2307/3315450?refreqid=search-gateway:c3a7da0239447971afa03cb0995530fd

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