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 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 ]