From Data to Probability Densities without Histograms

Details From Data to Probability Densities without Histograms From Data to Probability Densities without Histograms

2007-12-22

arxiv.org/abs/0712.3852v1

Comp. Phys. Commun. 179 (2008) 443-448

Physics

Data Analysis, Statistics and Probability

9 pages, 9 figures

Scientific paper

10.1016/j.cpc.2008.03.010

When one deals with data drawn from continuous variables, a histogram is often inadequate to display their probability density. It deals inefficiently with statistical noise, and binsizes are free parameters. In contrast to that, the empirical cumulative distribution function (obtained after sorting the data) is parameter free. But it is a step function, so that its differentiation does not give a smooth probability density. Based on Fourier series expansion and Kolmogorov tests, we introduce a simple method, which overcomes this problem. Error bars on the estimated probability density are calculated using a jackknife method. We give several examples and provide computer code reproducing them. You may want to look at the corresponding figures 4 to 9 first.

Affiliated with

Also associated with

No associations

Rating

From Data to Probability Densities without Histograms does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with From Data to Probability Densities without Histograms, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and From Data to Probability Densities without Histograms will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-425901