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statsmodels.expected_robust_kurtosis() in Python

  • Last Updated : 10 May, 2020

With the help of statsmodels.expected_robust_kurtosis() method, we can calculate the expected value of robust kurtosis measure by using statsmodels.expected_robust_kurtosis() method.

Syntax : statsmodels.expected_robust_kurtosis(ab, db)

Return : Return the four kurtosis value i.e kr1, kr2, kr3 and kr4.

Example #1 :
In this example we can see that by using statsmodels.expected_robust_kurtosis() method, we are able to get the expected value of robust kurtosis measure by using this method.






# import numpy and statsmodels
import numpy as np
from statsmodels.stats.stattools import expected_robust_kurtosis
    
# Using statsmodels.expected_robust_kurtosis() method
gfg = expected_robust_kurtosis()
    
print(gfg)

Output :

[3.0000000 1.23309512 2.58522712 2.90584695]

Example #2 :




# import numpy and statsmodels
import numpy as np
from statsmodels.stats.stattools import expected_robust_kurtosis
    
# Using statsmodels.expected_robust_kurtosis() method
gfg = expected_robust_kurtosis([12, 22], [6, 7])
    
print(gfg)

Output :

[3.0000000 1.23309512 1.23859789 1.0535188 ]

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