I tried using JMP for the same and get two distinct recommendations when
using the unscaled values.
When using the unscaled values, Log Normal appears to be best fit. fitdist
in R is unable to provide a fit in this case.
Compare Distributions
Show Distribution Number of Parameters -2*LogLikelihood AICc
X LogNormal 2 1016.29587 1020.50639
Johnson Sl 3 1015.21183 1021.6404
GLog 3 1016.29587 1022.72444
Exponential 1 1021.58662 1023.65559
Johnson Su 4 1015.21183 1023.9391
Gamma 2 1021.02475 1025.23528
Weibull 2 1021.50762 1025.71815
Extreme Value 2 1021.50762 1025.71815
Normal 2 Mixture 5 1042.55455 1053.66566
Normal 3 Mixture 8 1042.74433 1061.56786
Normal 2 1082.36992 1086.58045
However, when using the scaled values, Gamma appears to be best fit. I am
getting the same using R as well.
Compare Distributions
Show Distribution Number of Parameters -2*LogLikelihood AICc
X Gamma 2 -114.92911 -110.71858
Weibull 2 -113.54302 -109.3325
Extreme Value 2 -113.54302 -109.3325
Exponential 1 -108.01019 -105.94122
Johnson Sl 3 -104.69191 -98.263335
Johnson Su 4 -104.69191 -95.964634
GLog 3 -102.35037 -95.921798
LogNormal 2 -70.727608 -66.517082
Normal 2 Mixture 5 -77.349192 -66.238081
Normal 3 Mixture 8 -77.159407 -58.335878
Normal 2 -37.533813 -33.323287
What is the difference between the MLE methods in JMP and R??? Is it
advisable to go with the scaled values in R???
Thank you.
Ravi
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