Setting the negative eigenvalues (of which there may be many !) to zero
provides the least squares solution. Thus it gives \hat C such that
||C-\hat C||^2 is minimal over all psd matrices \hat C.
If C is a correlation matrix (as suggested below) then of course
C is already psd. If C is a matrix with merely diag(C)=I, then the
procedure above will usually not give diag(\hat C)=I.
At 14:57 -0500 07/06/2000, Rich Strauss wrote:
>At 03:46 PM 7/6/00 +0000, Christian A. Walter wrote:
>>Does anyone know if there is a structured way to adjust a negative
>>definite matrix such that it becomes semi-definite, while "minimizing"
>>the induced changes to the matrix?
>>
>>Cheers,
>>Christian
>
>I posed a similar question to edstat last fall. I was specifically
>concerned with non-positive-definite correlations matrices. Several people
>suggested to me the following numerical solution: get the eigenvectors and
>eigenvalues, set the negative eigenvalues to zero (there's generally only
>one that's negative) and proportionately adjust the others to maintain the
>same sum (total variance), and reconstruct the correlation matrix. This
>seems to work very well in practice. I've also done some simulations,
>beginning with a well-conditioned correlation matrix and gradually changing
>it until it becomes slightly ill-conditioned. The eigen procedure
>successfully 'corrects' the matrix.
>
>Rich Strauss
>
>
>
>
--
===
Jan de Leeuw; Professor and Chair, UCLA Department of Statistics;
US mail: 8142 Math Sciences Bldg, Box 951554, Los Angeles, CA 90095-1554
phone (310)-825-9550; fax (310)-206-5658; email: [EMAIL PROTECTED]
http://www.stat.ucla.edu/~deleeuw and http://home1.gte.net/datamine/
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