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
Tom Lane on 14 Dec 2017
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-')