P values for Normal Inverse Gaussian Distribution
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I would like to calculate P values for data with a Normal Inverse Gaussian Distribution. I have looked at options for the normal distribution and this post has been useful... http://www.mathworks.com/matlabcentral/newsreader/view_thread/298645
My data's distribution was best fit to a normal inverse gaussian distribution by the allfitdist() function.
Can I use the nigcdf() function http://www.mathworks.com/matlabcentral/fileexchange/10934-normal-inverse-gaussian-nig-distribution-updated-version/content/nigcdf.m to calculate the P values?
Thanks
Mel
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답변 (2개)
Star Strider
2012년 8월 15일
편집: Star Strider
2012년 8월 15일
If I understand your question correctly, the core MATLAB function erfc can do what you want, with a slight modification:
P = @(z) erfc(-z/sqrt(2))/2;
This is absolutely not original — I copied the idea directly from the documentation. This is equivalent to the normcdf function in the Statistics Toolbox.
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Star Strider
2012년 8월 15일
편집: Star Strider
2012년 8월 15일
I admit that I never encountered any of those. They're not in any of my reference texts on random processes, mathematical biology, experiment design, or Kalman filters. When you mentioned z-scores and getting p-values from them, I thought you were referring to the standard normal distribution and its inverse.
I'm not quite sure what you're doing, but my only caution is to be sure that your data meet the assumptions of the distribution you choose. The fact that a distribution ‘fits’ does not imply that it is correct for your data or your application.
Gabriel Bruneau
2012년 8월 15일
If I understand correctly your question, I think that the function norm_prb.m in the Econometrics Toolbox by James P. LeSage does exactly what you want.
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