How to generate random numbers between min and max value with gevrnd?I

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snr matlb 2020-6-30

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评论： snr matlb 2020-7-2

I have 16 value with distributed by generalized extreme value. I have k, sigma and mu of this data. I want to increase the data with this distribution between 20 and 21. How can I do it, may I get your help with code sharing?

Thanks

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Walter Roberson 2020-7-1

Do I understand properly that currently you have the situation where you have a continuous distribution that includes the range 20 to 21, and that you would like to create a new distribution with increased probability between 20 and 21? If the probability from -inf to 20 is currently p1, and the probability from 20 to 21 is p2, and the probability from 21 to infinity is currently p3, that instead of that [p1, p2, p3] situation, you would like a situation where middle section was C times higher, so your distribution would be in sections with probabilities

[(C*p2 - 1)/(p2 - 1) * p1, C*p2, (C*p2 - 1)/(p2 - 1) * p3]

If so then note that the pdf would no longer be continuous and that the sigma and mu would change.

snr matlb 2020-7-1

Actually, I have a data which is distributed generalized extreme value. The data between 18-20. I have k, sigma and mu of this data. Now, I want to generate random numbers with this k,sigma and mu (with same distribution) but between 20 and 21.

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Walter Roberson 2020-7-1

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https://www.mathworks.com/help/stats/prob.normaldistribution.truncate.html

truncate() the distribution. That will automatically re-scale the pdf so that the probability integral over the given range becomes 1.

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dpb 2020-7-1

编辑：dpb 2020-7-1

"The data between 18-20. I have k, sigma and mu of this data. Now, I want to generate random numbers with this k,sigma and mu (with same distribution) but between 20 and 21."

What does that mean, precisely?

You just want to shift the mean and keep the same dispersion or do you think you can bound the distribution between limits?

A GEV distribution is one of a class of EXTREME VALUE distributions...they can be truncated on upper or lower bound depending upon the value of sigma/k + mu, if

k<0, then p(x)=0, x > -sigma/k+u,
k>0, then p(x)=0, x < -sigma/k+u.

If k=0, then is unbounded both directions.

You can find a set of parameters that will truncate one side or the other, but simply can't have both truncated.

A set of data such as you describe really doesn't seem likely at all to have come from an extreme value population to begin with...that's just not what would expect to see.

What are these data and where did they come from? What makes you think they should be EV other than you can fit the model to any set of data?

snr matlb 2020-7-2

Actually, if I expand hour pies to provide some overcapacities I may get some delay to solve, not miliseconds actually working with minutes. So, I will expand the data but some how with trying to save the distribution as real data to based on real data for further generated. It is better that pure generated numbers I think.

snr matlb 2020-7-2