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MLE: NMaximize vs FindDistributionParameters

Posted 11 years ago
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POSTED BY: Sandu Ursu
7 Replies
Posted 11 years ago

Thanks, Marco. Usually the Hessian (or an approximation of it) is created during the iterative process for finding maximum likelihood estimates so I would think that it shouldn't be too much trouble to make that available (or its inverse when it exists) as an option for FindDistributionParameters.

If Wolfram is packaging these high level functions to be of value other than being a good way to obtain starting values for a more detailed analysis, I would consider that providing these estimated standard errors is essential.

Jim

POSTED BY: Jim Baldwin
Posted 11 years ago
POSTED BY: Jim Baldwin
POSTED BY: Marco Thiel
POSTED BY: Bruce Miller
Posted 11 years ago

I continue to make more than my fair share of programming mistakes (especially with Mathematica). The only ways I know how to minimize such occurrences are to (1) perform lots of checks (Do the probability functions sum/integrate to 1 for arbitrary parameters?), and (2) (probably the best technique) Grab anybody and explain your problem with the code in front of both of you. Then either that person will catch the obvious (upper/lowercase, spelling, etc.) or while you're explaining you'll say "Oh, nevermind." because the issue will become obvious.

POSTED BY: Jim Baldwin
Posted 11 years ago

The two solutions are too far apart to be considered similar. The issue is that a factor of 1/2 is missing from the pdf definition. To match the likelihood the pdf needs to be

pdf[x_] := 
  Sqrt[(1 - \[Rho])/\[Rho]]
    Exp[(inN[x]^2 - ((Sqrt[1 - \[Rho]] inN[x] - inN[PD])/
        Sqrt[\[Rho]])^2)/2];

Now the result will be

{PD -> 0.013438593418697627473, \[Rho] -> 0.10995316859509961784}

which matches

{137.731, {PD -> 0.0134386, \[Rho] -> 0.109953}}
POSTED BY: Jim Baldwin
Posted 11 years ago
POSTED BY: Sandu Ursu
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