turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

Find a Community

- Home
- /
- SAS Programming
- /
- Base SAS Programming
- /
- Using simulation to compute p-values

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

06-15-2017 09:18 AM

Hello SAS users,

I used the SEVERITY procedure to fit and test data dstribution.

Among all the stats and plots available, one can have the critical values for the Anderson-Darling test.

Now, I wonder about a way to compute the relative p-value for such statistics/test.

Can you suggest a way to compute that?

The probability distribution is the Geenralyzed Pareto distribution.

Thanks all in advance for the help!!

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

Posted in reply to Quantopic

06-15-2017 10:14 AM

Please supply sample data and code that show what you are trying to accomplish.

The title of this topic includes the phrase "using simulation", but you do not mention simulation in your question. The typical simulation approach to estimate a p-value is

1. Compute a statistic for the observed data

2. Simulate a sample from a known population that is appropriate for the observed data. (aka, simulate from the "null distribution.")

3. Compute the same statistic for the simulated data.

4. Repeat Steps 2-3 many times.

5. Compare the observed statistic to the distribution of the statistics on the simulated samples. The Monte Carlo p-value is the proportion of simulated statistics that are more extreme than the observed statistic.

The same technique is used to estimate a p-value for a bootstrap distribution. For a simulation example, you can see the article on using Monte Carlo simulation to estimate the p-value for the chi-square test.