[SciPy-Dev] weird results in frozen distribution fit

[K.-Michael Aye]

So, Josef, are you saying that we should not use frozen fits for the 
moment? I consider you one of the authorities on these matters, me as a 
fitting beginner am mostly lost in the choices I have.
Do we have alternatives/work-arounds?

Michael

On 2011-10-10 22:22:27 +0000, josef.pktd@gmail.com said:

> Given a report on the scipy-user mailing list
> http://projects.scipy.org/scipy/ticket/1536 I started for the first
> time to look at some examples with the frozen fit introduced in scipy
> 0.9.  I just picked randomly some distributions that came to mind,
> except for the reported lognorm.
> 
> some cases look ok, maybe. Some cases look "weird"
> 
> Josef
> 
> lognorm true
> 0.25 0.0 20.0
> estimated, floc=0, loc=0
> [ 2.1221  0.      2.4403] [  0.2303  -1.8759  21.9986]
> [ 2.1454  0.      2.3763] [  0.2446   0.0305  19.9945]
> [ 2.1274  0.      2.414 ] [  0.2469   0.0296  19.9415]
> [ 2.1362  0.      2.3897] [  0.2525   0.4579  19.4758]
> [ 2.1334  0.      2.4104] [  0.2484   0.0298  20.0291]
> [ 2.1033  0.      2.475 ] [  2.5335e-01   1.0858e-02   1.9959e+01]
> [ 2.1266  0.      2.414 ] [  0.2662   1.3626  18.5458]
> [ 2.1316  0.      2.4176] [  0.2491   0.0299  20.0509]
> [ 2.0897  0.      2.5211] [  0.276    1.4881  18.5202]
> [ 2.1382  0.      2.3738] [  0.2465   0.0301  19.8241]
> gamma true
> 2 20 10.0
> estimated, floc=0, loc=0
> [  2.0945  20.       9.6182] [  1.9864  20.0832  10.2119] [  2.0317
> 20.      10.    ]
> [  2.0084  20.      10.101 ] [  1.9364  20.1738  10.41  ] [  2.0241
> 20.      10.    ]
> [  2.1359  20.       9.1555] [  2.0353  19.8413   9.8974] [  1.9937
> 20.      10.    ]
> [  2.2296  20.       8.6785] [  2.152   19.8834   9.1795] [  1.9948
> 20.      10.    ]
> [  1.8822  20.      10.8936] [  2.0389  19.9836   9.8365] [  2.0104
> 20.      10.    ]
> [  1.8304  20.      11.3802] [  2.0341  20.169    9.7599] [  2.0214
> 20.      10.    ]
> [  1.972   20.       9.7958] [  1.9107  20.0777  10.1223] [  1.9409
> 20.      10.    ]
> [  1.6211  20.      13.3028] [  1.9636  20.1903  10.1156] [  2.0103
> 20.      10.    ]
> [  2.0413  20.       9.8165] [  2.0238  19.9456   9.9794] [  2.0121
> 20.      10.    ]
> [  2.0087  20.      10.1362] [  2.0128  20.1212   9.9924] [  2.0299
> 20.      10.    ]
> normal true
> 0.0 2.0
> estimated, floc=0, loc=0, fscale=2
> [ 0.      2.0024] [-0.0417  2.0024] [-0.0417  2.    ]
> [ 0.      2.0003] [-0.0879  2.0003] [-0.0879  2.    ]
> [ 0.      1.9466] [-0.0133  1.9466] [-0.0133  2.    ]
> [ 0.      1.9726] [-0.036   1.9726] [-0.0359  2.    ]
> [ 0.      2.0007] [-0.0194  2.0007] [-0.0195  2.    ]
> [ 0.      1.9565] [ 0.0338  1.9565] [ 0.0337  2.    ]
> [ 0.      1.9674] [ 0.0288  1.9674] [ 0.0289  2.    ]
> [ 0.      1.9962] [ 0.0051  1.9962] [ 0.0051  2.    ]
> [ 0.      1.8901] [ 0.0179  1.8901] [ 0.0179  2.    ]
> [ 0.      2.0017] [ 0.0307  2.0017] [ 0.0307  2.    ]
> chi2 true
> 10 0.0 2.0
> estimated, floc=0, loc=0
> [ 3.8459  0.      6.1457] [ 10.9616  -0.6938   1.8646]
> [ 3.8314  0.      6.3129] [ 10.4894  -0.295    1.9516]
> [ 3.7449  0.      6.4353] [ 9.7304 -0.0364  2.0654]
> [ 3.8094  0.      6.2268] [ 10.4314  -0.3388   1.9283]
> [ 3.69    0.      6.5633] [ 8.4951  1.1035  2.2316]
> [ 3.6475  0.      6.6415] [ 8.3315  0.8241  2.3152]
> [ 3.7288  0.      6.4983] [ 9.4114  0.2128  2.1185]
> [ 3.8005  0.      6.2852] [ 10.0038  -0.0154   1.9917]
> [ 3.7384  0.      6.4327] [ 8.4504  1.1931  2.224 ]
> [ 3.9063  0.      6.0929] [ 10.3413   0.2105   1.902 ]
>>>> 
> 
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