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

Thanks!

Answer by 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-')

Tam Ho
on 28 Dec 2017

I am not sure how to perform normalization as you suggested using code - any suggestions?

In regards to using linspace, in your previous message linspace was used to create the support vector for the kernel I guess. I'm not too versed in using kernel density, just trying apply the method - what is the difference between using linspace like you did and setting 'bandwidth',0.5 ?

Thanks a lot!

Brendan Hamm
on 29 Dec 2017

To follow up on Tom's post:

The ksdensity function includes a Support input argument. You could not use the exact min and max for the Support, but if you extend that range out slightly it will work.

x = gamrnd(2,3,1000,1);

X = linspace(0,40,1000);

n = 1e5;

delta = 0.01; % Factor for expanding the Support

Support = [min(x)-delta,max(x)+delta];

X = linspace(Support(1),Support(2),n);

F = ksdensity(x,X,'Function','cdf','Support',Support);

You can perform a numerical integration with the trapz function:

f = ksdensity(x,X,'Function','pdf','Support',Support);

I = trapz(X,f) % As n-> Inf, I -> 1

Bandwidth is also an option, but when you provide a bounded support (as done above) a log transformation is applied to the data and the bandwidth applies on this scale. So you may need to check if the requirements are on the original scale (which I assume they are).

F = ksdensity(x,X,'Function','cdf','Support',Support,'Bandwidth',0.5);

The points for X from linspace are simply the points to evaluate the pdf/cdf and do not change the fitting which is done only on the underlying data x.

Tam Ho
on 29 Dec 2017

Thanks a lot!

I have the following code which aims to solve for theta - roots of first derivative of the pdf

syms theta %create symbolic variable theta

assume(theta,'real') %theta is real

pf = poly2sym(fit.coeff,theta); %calling our fitted polynomials

g = diff(pf,theta);

g0=solve(g,theta);

theta = double(g0);

What I did was plot the pdf from kernel estimation, using Basic Fitting to obtain the function, then solve for theta.

However, Basic Fitting doesn't fit very well with the pdf. How can I obtain pdf and take derivative without producing too much residuals?

Additionally, theta has to follow three conditions: -smaller than the highest pdf value -pdf evaluation of theta must be smaller than 0.8 times of that of the highest pdf value -integral from min x value to theta of pdf must be larger than 0.05

θ < μ, f(θ)<0.8·f(μ) and ∫ f (x)dx > 0.05 from min x to θ.

Is there a way to incorporate loop and boolean to automate solving for theta.

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