statsmodels.expected_robust_kurtosis() in Python
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 as np
from statsmodels.stats.stattools import expected_robust_kurtosis
gfg = expected_robust_kurtosis()
print (gfg)
|
Output :
[3.0000000 1.23309512 2.58522712 2.90584695]
Example #2 :
import numpy as np
from statsmodels.stats.stattools import expected_robust_kurtosis
gfg = expected_robust_kurtosis([ 12 , 22 ], [ 6 , 7 ])
print (gfg)
|
Output :
[3.0000000 1.23309512 1.23859789 1.0535188 ]
Last Updated :
10 May, 2020
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