Re: [ojAlgo-user] covariance matrix

[Anders P. <an...@op...>]

On 18 jul 2007, at 12.38, Dimitri Pourbaix wrote:

> On Wed, 18 Jul 2007, Anders Peterson wrote:
>
>> Both QR and SVD do least squares fitting. Just call solve(?) and  
>> that's what you get. QR is faster and often good enough, but in  
>> some cases it could have numerical problems that the SVD probably  
>> will not have.
>
> Well, in my case, robustness is more important than speed.

If accuracy is what you want perhaps you should try BigQR

http://ojalgo.org/generated/org/ojalgo/matrix/decomposition/BigQR.html

/Anders


>> Exactly how do you calculate the parameter covariance matrix using  
>> the SVD?
>
> Starting with V and S the two matrices from SVD, Sv is the double[]  
> array
> of S
>
> double[][] Sm2v = new double[dimModel][dimModel];
> for (int i=0;i<dimModel;i++) {
> 	for (int j=0;j<dimModel;j++) {
> 		Sm2v[i][j]=0.0;
> 	}
> 	Sm2v[i][i]=1.0;
> }
> for (int j = 0; j < dimModel; j++) {
> 	Sm2v[j][j] /= (Sv[j] * Sv[j]);
> }
> Matrix Sm2 = new Matrix(Sm2v);
> covMat = V.times(Sm2.times(V.transpose()));
>
> Regards,
>  Dim.
> ---------------------------------------------------------------------- 
> ------
> Dimitri Pourbaix                         *
> Institut d'Astronomie et d'Astrophysique *      Don't worry, be happy
> CP 226, office 2.N4.211, building NO     *         and CARPE DIEM.
> Universite Libre de Bruxelles            *
> Boulevard du Triomphe                    *      Tel : +32-2-650.35.71
>  B-1050 Bruxelles                        *      Fax : +32-2-650.42.26
> http://sb9.astro.ulb.ac.be/~pourbaix     *  
> mailto:pou...@as...
>
>