I would like to find the CDF from an estimated PDF. This PDF was estimated from Kernel Density Estimation (with a Gaussian kernel using a 0.6 width window).

I know, in theory, that the CDF can be estimated as: $F_{X}(x) = \int_{-\infty}^{x}f(t)dt$

Is it possible to apply this integral directly on the estimated pdf? In this case, I am using python.

thanks in advance.

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    $\begingroup$ What you know is the height of the density at each point. So I think you need something like a Riemann -Stieltjes integral through a rectangular approximation. $\endgroup$ Aug 4, 2017 at 22:03
  • $\begingroup$ Isn't there a more straightforward way to do it instead of using an integral? Just asking. $\endgroup$ Aug 5, 2017 at 0:45
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    $\begingroup$ You don't know what f(t) is and the CDF is a integral. What else do you think you could do? $\endgroup$ Aug 5, 2017 at 1:43
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    $\begingroup$ @Michael Isn't the whole point that the OP has an estimate of $f$? The question therefore concerns the properties of a procedure that estimates a CDF by integrating an estimate of the PDF. $\endgroup$
    – whuber
    Aug 7, 2017 at 16:29
  • $\begingroup$ The estimate is not in closed form so I am just saying that it has to be calculated numerically. The normal distribution is also not in closed form but has been numerically integrated already. I was just addressing the idea that it could be computed "directtly". $\endgroup$ Aug 7, 2017 at 16:55

1 Answer 1


There's no need to integrate anything if you know the cdf of the kernel itself. I believe this is straightforward for all the common kernels.

Note that

  1. a KDE is a mixture density

  2. the cdf of a mixture is the mixture of the cdfs.

that is, if $\hat{f}(x)=\frac{1}{n}\sum_i f_i(x)$ is your KDE at $x$, then $\hat{F}(x)=\frac{1}{n}\sum_i F_i(x)$.

Take a Gaussian kernel for example. If $x_i$ are your observations, $f_i$ is $\frac{1}{\sigma} \phi(\frac{x- x_i}{\sigma})$ and $F_i=\Phi(\frac{x-x_i}{\sigma})$, where commonly $\sigma$ is defined as the bandwidth (in some implementations the bandwidth may be some multiple of $\sigma$).

Indeed, R does that (defines bandwidth = $\sigma$) for all its kernels, not just the Gaussian one. But it's easy as long as you can convert a bandwidth to the parameters of the kernel so you can call a function for the cdf.

So you can evaluate the cdf of your mixture at any $x$ in linear time. If you need it to be able to calculate $\hat{F}$ fast, you could evaluate it over a grid (fine enough to get sufficient accuracy), and use interpolation in between (e.g. in R this is easily done with approxfun; no doubt Python has a convenient way to do something similar)

Here's an example of a plot of a kde and cdf for a Gaussian kernel.

kernel density estimate and corresponding cdf

Here's the code I used (it was done in R - this is a quick kludge to show the idea, a proper function would be checking arguments, providing better info, labelling axes, letting you specify the kernel and so on). The workhorse is the third line, which defines the function that does all the actual calculation of the cdf, everything else is details of data or plotting.

x <- c(11,12,16) #data
xx <- seq(7,20,.1) # plot values for the cdf
kdecdfnorm <- function(x,xdat,bw) rowMeans(pnorm(outer(x,xdat,"-"),0,bw)) #cdf of KDE
opar <- par() # save graphics parameter settings
par(mfrow=c(1,2)) # 1 x 2 plot grid
kde <- density(x)
bw <- kde$bw 
par(opar) # restore graphics parameters

How does that rowMeans(pnorm(outer(x,xdat,"-"),0,bw)) work?

  • rowMeans is just doing $\frac{1}{n}\sum_{i=1}^{n}$ of its argument

  • pnorm is computing the cdf of the Gaussian kernel terms, with bandwidth at its last argument

  • the first argument to pnorm is just $x-x_i$ over the data values ($x_i$) and the various x's we want to find the curve at

which is to say we're just computing $\frac{1}{n}\sum_i \Phi(\frac{x-x_i}{\sigma})$ in a quite direct way, across whatever values for $x$ we want to calculate it at.


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