Recent content by steviekm3

If you take Wiener process and break it up into any amount of intervals eg 2,3,1000,1000000 etc. Then withen each interval you have in effect another Wiener process. In this sense it is like a fractal. No matter how small the interval, you always have another Wiener process. So one thing...

why what is normally distributed ?

If you don't multiply your delta z will be too big. Suppose t is really small then certainly delta z does not have distribution N(0,1).

My apologies here, the formula should have been: (mean absolute deviation) /sqrt(2/pi) Then to add bias correction: (mean absolute deviation) /sqrt(2*f/pi) where f = (n-1)/n

I only need std deviation because the library function that I'm using takes standard deviation as an argument. I could adjust the regular estimator for standard deviation using: "en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation" All of this is more for interest sake...

For the particular application I'm working on, I'm looking for unbiased estimator of standard deviation. The reason is because I have sample points in which to infer the distribution. Once I have the distribution I have to run a simulation on it and the simulation generates random normal...

Okay I got some more time to work on this. What I found out is I believe that a correction factor has to be added to the estimator. When I add in this correction factor the estimator should be an unbiased estimator of the standard deviation. Here is code that compares standard estimator...

I ran some simulations as well and I cannot see the radical underestimation of variance of population. The term radical is subjective anyhow, do you have a more quantifiable description ?

I don't think my calculation uses the sample standard deviation formula. For example suppose these are the numbers in series: 1, 2, 3 I would find mean of these which is 2. and then find average of |1-2|,|2-2|,|3-2| which is 2/3 Then the estimate for std deviation of population...

Suppose we have a normal distribution and a sample of n values from the normal distribution. To estimate the variance we can use the standard sample variance formula ( average squared distance from the mean divided by either n ( biased estimator ) or n-1 ( unbiased estimator ) ). There is...