Dyadic kernel density estimation in Julia.
This repository provides a Julia package which implements the methods for dyadic kernel density estimation detailed in Cattaneo, Feng and Underwood, 2022. In particular, the package provides the capability for computing
- Point estimates of a univariate dyadic density function
- Pointwise confidence intervals for the density
- Bonferroni-corrected confidence intervals
- Uniform confidence bands
- A rule-of-thumb bandwidth selector
- Counterfactual dyadic density estimation
The currently supported kernels are
- Epanechnikov, order 2
- Epanechnikov, order 4
Install from the Julia General registry by starting a Julia interactive session and running
] add DyadicKDEAlternatively install from source by running
] add "https://github.com/WGUNDERWOOD/DyadicKDE.jl.git"The package can then be loaded with
using DyadicKDEand tested (this may take a few minutes) with
] test DyadicKDEDyadicKDE.jl requires Julia 1.x and depends on several other Julia packages listed in Project.toml.
# load package
using DyadicKDE
# specify parameters
n_data = 100
kernel_name = "epanechnikov_order_2"
evals = collect(range(-2.0, stop=2.0, length=10))
sdp_solver = "cosmo"
n_resample = 1000
significance_level = 0.05
p = [0.25, 0.0, 0.75]
# make data and get bandwidth
W = make_data(n_data, p)
h_ROT = estimate_ROT_bandwidth(W, "epanechnikov_order_2")
# fit dyadic kernel density estimator
est = DyadicKernelDensityEstimator(
kernel_name, h_ROT, significance_level,
n_resample, sdp_solver, evals, W, Dict())
fit(est)
# display properties of estimator
display(est)
# display evaluation points
display(evals')
# display point estimates
display(est.fhat')
# display pointwise confidence intervals
display(est.pci)
# display uniform confidence band
display(est.ucb)Please refer to the replication README.