Fast adaptive kernel density estimation in one-dimension in one m-file;
Provides optimal accuracy/speed trade-off. To increase speed when dealing with "big data",
simply reduce the "gam" parameter; Typically "gam=n^(1/3)", where "n" is the length of data.
X - data as a 'n' by '1' vector;
grid - (optional) mesh over which density is to be computed;
default mesh uses 2^12 points over range of data;
gam - (optional) cost/accuracy trade-off parameter, where gam<n;
default value is gam=ceil(n^(1/3))+20; larger values
result in better accuracy, but reduce speed;
to speedup the code, use smaller "gam";
pdf - the value of the estimated density at 'grid'
data=[exp(randn(10^3,1))]; % log-normal sample
Note: If you need a very fast estimator use my "kde.m" function.
This routine is more adaptive at the expense of speed. Use "gam" to control a speed/accuracy tradeoff.
Kernel density estimation via diffusion
Z. I. Botev, J. F. Grotowski, and D. P. Kroese (2010)
Annals of Statistics, Volume 38, Number 5, pages 2916-2957.
Zdravko Botev (2021). adaptive kernel density estimation in one-dimension (https://www.mathworks.com/matlabcentral/fileexchange/58309-adaptive-kernel-density-estimation-in-one-dimension), MATLAB Central File Exchange. Retrieved .
Could anyone provide any resources explaining this method? There doesn't seem to be any mention about it in the linked paper, nor have I been able to find it elsewhere.
I had found the paper this code was based on and was trying to implement it using symbolic functions. It appeared this was much too computationally difficult, and as I was about to give up, I found this code. It appears to work very well, and there's nobody better to get it from than the person who published the paper!
Perfect code!Thank you so much!
Excellent practical results. I'm curious what is the form of the diffusion equation implemented in the code?
It seams line 42 has an error in default gamma:
gam > n when n < 23
so in that case mu=X(perm(1:gam),:) can not be indexed
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