Hi all,

I have a matrix that has many NaN values. As soon as one of the columns has
a missing (NaN) value the covariance estimation gets thrown off.

Is there a robust way to do this?

Thanks,
Sachin

a=array(rnorm(9),dim=c(3,3))> a            [,1]       [,2]      [,3]
[1,] -0.79418236  0.7813952  0.855881
[2,] -1.65347906 -1.9462446 -0.376325
[3,] -0.03144987  0.6756862 -1.879801> a[3,2]=NANError: object 'NAN'
not found> a[3,2]=NaN> a            [,1]       [,2]      [,3]
[1,] -0.79418236  0.7813952  0.855881
[2,] -1.65347906 -1.9462446 -0.376325
[3,] -0.03144987        NaN -1.879801> cov(a)           [,1] [,2]       [,3]
[1,]  0.6585217   NA -0.5777408
[2,]         NA   NA         NA
[3,] -0.5777408   NA  1.8771214

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