Create CDF for Anderson Darling test for Octave forge Statistics package function

I am using Octave and I would like to use the anderson_darling_test from the Octave forge Statistics package to test if two vectors of data are drawn from the same statistical distribution. Furthermore, the reference distribution is unlikely to be "normal". This reference distribution will be the known distribution and taken from the help for the above function " 'If you are selecting from a known distribution, convert your values into CDF values for the distribution and use "uniform'. "

My question therefore is: how would I convert my data values into CDF values for the reference distribution?

Some background information for the problem: I have a vector of raw data values f开发者_如何转开发rom which I extract the cyclic component (this will be the reference distribution); I then wish to compare this cyclic component with the raw data itself to see if the raw data is essentially cyclic in nature. If the the null hypothesis that the two are the same can be rejected I will then know that most of the movement in the raw data is not due to cyclic influences but is due to either trend or just noise.

If your data has a specific distribution, for instance beta(3,3) then

p = betacdf(x, 3, 3)

will be uniform by the definition of a CDF. If you want to transform it to a normal, you can just call the inverse CDF function

x=norminv(p,0,1)

on the uniform p. Once transformed, use your favorite test. I'm not sure I understand your data, but you might consider using a Kolmogorov-Smirnov test instead, which is a nonparametric test of distributional equality.

Your approach is misguided in multiple ways. Several points:

• The Anderson-Darling test implemented in Octave forge is a one-sample test: it requires one vector of data and a reference distribution. The distribution should be known - not come from data. While you quote the help-file correctly about using a CDF and the "uniform" option for a distribution that is not built in, you are ignoring the next sentence of the same help file:

Do not use "uniform" if the distribution parameters are estimated from the data itself, as this sharply biases the A^2 statistic toward smaller values.

So, don't do it.

• Even if you found or wrote a function implementing a proper two-sample Anderson-Darling or Kolmogorov-Smirnov test, you would still be left with a couple of problems:

  1. Your samples (the data and the cyclic part estimated from the data) are not independent, and these tests assume independence.

  2. Given your description, I assume there is some sort of time predictor involved. So even if the distributions would coincide, that does not mean they coincide at the same time-points, because comparing distributions collapses over the time.

  3. The distribution of cyclic trend + error would not expected to be the same as the distribution of the cyclic trend alone. Suppose the trend is sin(t). Then it never will go above 1. Now add a normally distributed random error term with standard deviation 0.1 (small, so that the trend is dominant). Obviously you could get values well above 1.

We do not have enough information to figure out the proper thing to do, and it is not really a programming question anyway. Look up time series theory - separating cyclic components is a major topic there. But many reasonable analyses will probably be based on the residuals: (observed value - predicted from cyclic component). You will still have to be careful about auto-correlation and other complexities, but at least it will be a move in the right direction.