Contents
How to compare two distributions using discrete KS?
The following is a procedure to conduct the discrete KS test for two samples: Find the min and max of the combined sample to define our range. e.g. for a sample size of 500, we can expect 25 samples per bin by choosing 20 buckets.
Is the Poisson distribution a discrete probability distribution?
The Poisson distribution The Poisson distribution is a discrete probability distribution for the countsof events that occur randomly in a given interval of time (or space). If we letX= The number of events in a given interval. Then, if the mean number of events per interval is
Which is the best description of a parametric distribution?
A parametric distribution is an abstract mathematical form, or characteristic shape. Some of these mathematical forms arise naturally as a consequence of certain kinds of data-generating processes, and when applicable these forms are especially plausible candidates for concisely representing variations in a set of data.
How to compare two distributions in real life?
The red line is the actual test statistic and the green line is the test statistic for 1000 random normal variables. By inserting the KS test statistic for the actual sample (i.e. the red line), we can see that the actual KS test statistic is contained inside the distribution.
How to measure the difference between two distributions?
It all depends on how you define a difference between two distributions. To give you two ideas: A Kolmogorov-Smirnov test is a non-parametric test, that measures the “distance” between two cumulative/empirical distribution functions.
How to compare a sample with a distribution?
When we compare a sample with a theoretical distribution, we can use a Monte Carlo simulation to create a test statistics distribution. For instance, if we want to test whether a p-value distribution is uniformly distributed (i.e. p-value uniformity test) or not, we can simulate uniform random variables and compute the KS test statistic.
How to compare two p-value distributions in practice?
For instance, if we want to test whether a p-value distribution is uniformly distributed (i.e. p-value uniformity test) or not, we can simulate uniform random variables and compute the KS test statistic. By repeating this process 1000 times, we will have 1000 KS test statistics, which gives us the KS test statistic distribution below.