[SciPy-User] Confidence interval for bounded minimization
Wed Feb 22 16:02:42 CST 2012
On Wed, Feb 22, 2012 at 8:48 PM, <firstname.lastname@example.org> wrote:
> On Wed, Feb 22, 2012 at 3:26 PM, Greg Friedland
> <email@example.com> wrote:
>> Is it possible to calculate asymptotic confidence intervals for any of
>> the bounded minimization algorithms? As far as I can tell they don't
>> return the Hessian; that's including the new 'minimize' function which
>> seemed like it might.
> If the parameter ends up at the bounds, then the standard statistics
> doesn't apply. The Hessian is based on a local quadratic
> approximation, which doesn't work if part of the local neigborhood is
> out of bounds.
> There is some special statistics for this, but so far I have seen only
> the description how GAUSS handles it.
> In statsmodels we use in some cases the bounds, or a transformation,
> just to keep the optimizer in the required range, and we assume we get
> an interior solution. In this case, it is possible to use the standard
> calculations, the easiest is to use the local minimum that the
> constraint or transformed optimizer found and use it as starting value
> for an unconstrained optimization where we can get the Hessian (or
> just calculate the Hessian based on the original objective function).
Some optimizers compute the Hessian internally. In those cases, it
would be nice to have a way to ask them to somehow return that value
instead of throwing it away. I haven't used Matlab in a while, but I
remember running into this as a standard feature at some point, and it
was quite nice. Especially when working with a problem where each
computation of the Hessian requires an hour or so of computing time.
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