I think that Dmitri overstates his case a bit.
This multiplication in observation space works for some algorithms, not for
others. Ordinary least squares regression is somewhat of an exception
here. Logistic regression is a simple counter-example.
It is still useful to have a vector weight and
Hi,
The existing code is :
- package o.a.c.math.estimation:
Levenberg-Marquardt method for weighted least square minimization
of a vector of residuals
Either one considers the full weighting matrix (including potential
correlation between observations) or one does not account for any wei
ell since it is more specific
- the user knows exactly what they will find in that package.
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Phil Steitz a écrit :
> Luc Maisonobe wrote:
>> Hello,
>>
>> Since release of 1.2, several people have asked for advices on using the
>> estimation and optimization packages imported from mantissa. This showed
>> these packages were poorly designed (you can blame me for that). After
>> one of the d
Luc Maisonobe wrote:
Hello,
Since release of 1.2, several people have asked for advices on using the
estimation and optimization packages imported from mantissa. This showed
these packages were poorly designed (you can blame me for that). After
one of the discussions on this topic, issue MATH-17
Hello,
Since release of 1.2, several people have asked for advices on using the
estimation and optimization packages imported from mantissa. This showed
these packages were poorly designed (you can blame me for that). After
one of the discussions on this topic, issue MATH-177
(https://issues.apach
