If it exists, how can I get a distribution with the same mean, variance and kurtosis but different skewness?











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I would like to know if there exists probability distributions (or families) such that I can hold constant mean, variance and kurtosis but can allow skewness to vary?
Thank you for your help






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    Sounds like a job for clever folk on stats.stackexchange.com
    – Mark Setchell
    Nov 4 at 10:23















up vote
0
down vote

favorite












I would like to know if there exists probability distributions (or families) such that I can hold constant mean, variance and kurtosis but can allow skewness to vary?
Thank you for your help






probability







share|improve this question




















  • 1




    Sounds like a job for clever folk on stats.stackexchange.com
    – Mark Setchell
    Nov 4 at 10:23













up vote
0
down vote

favorite









up vote
0
down vote

favorite











I would like to know if there exists probability distributions (or families) such that I can hold constant mean, variance and kurtosis but can allow skewness to vary?
Thank you for your help






probability







share|improve this question















I would like to know if there exists probability distributions (or families) such that I can hold constant mean, variance and kurtosis but can allow skewness to vary?
Thank you for your help






probability




probability






share|improve this question















share|improve this question













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edited Nov 4 at 11:34

























asked Nov 4 at 9:56









Philou

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  • 1




    Sounds like a job for clever folk on stats.stackexchange.com
    – Mark Setchell
    Nov 4 at 10:23














  • 1




    Sounds like a job for clever folk on stats.stackexchange.com
    – Mark Setchell
    Nov 4 at 10:23








1




1




Sounds like a job for clever folk on stats.stackexchange.com
– Mark Setchell
Nov 4 at 10:23




Sounds like a job for clever folk on stats.stackexchange.com
– Mark Setchell
Nov 4 at 10:23

















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