Kernel Density estimation with chosen bandwidth, then normalize the density function (cdf) so that integral of cdf from min to max equal to 1 ; then take the first and second derivative of the cdf
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I've tried using kde(data,n,MIN,MAX) and [f,xi] = ksdensity(x) over my data points.
I haven't figure out how to retrieve the cdf (density function).
I've tried using linear fit on the density data points (I got from using [density,cdf]=kde(y,1000,min(y),max(y))
but wonder if there is another method to approach finding the kernel density cdf assuming normal distribution with chosen bandwidth (standard deviation) 0.5
回答 (1 件)
Tom Lane 2017 年 12 月 14 日
You seem to want to do a number of things including integrating and specifying a bandwidth. Maybe this will get you started.
Here's an example looking at a kernel density estimate from a gamma random variable and comparing it with the distribution used to generate the data.
>> x = gamrnd(2,3,1000,1);
>> X = linspace(0,40,1000);
>> f = ksdensity(x,X);
>> plot(X,gampdf(X,2,3),'r:', X,f,'b-')
Usually "cdf" is used to describe the cumulative distribution function rather than the density (pdf). Here's how to get that.
>> F = ksdensity(x,X,'Function','cdf');
>> plot(X,gamcdf(X,2,3),'r:', X,F,'b-')