I am using a mathematical function to estimate the covariance matrix for some process from the variances and then using this covariance matrix in a generalised least squares estimation of the slope and intercept of the data.

However, sometimes I get nonsense values (i.e. 10e123, when the slope should be ~1) for the slope. Adding a small amount of Gaussian noise to the variances when this happens seems to remove the problem, but it is a bit inelegant of a solution. I have checked and there is no noticeable difference between the covariance matrix condition number/determinant/condition number when I get the erroneous result and not.

I was wondering if anyone knows how I either (1) detect when I will get an erroneous result so that I can add the noise before the slope is calculated or (2) have any ideas how to remove the erroneous result in the first place (to this aim, I have tried smoothing the matrix and scaling the diagonal amount other things with no luck).

I am using a mathematical function to estimate the covariance matrix for some process from the variances and then using this covariance matrix in a generalised least squares estimation of the slope and intercept of the data. (Although the covariance matrix comes from a function, it is not modified as a part of the GLS estimation.)

However, sometimes I get nonsense values (i.e. 10e123, when the slope should be ~1) for the slope. Adding a small amount of Gaussian noise to the variances when this happens seems to remove the problem, but it is a bit inelegant of a solution. I have checked and there is no noticeable difference between the covariance matrix condition number/determinant/condition number when I get the erroneous result and not.

I was wondering if anyone knows how I either (1) detect when I will get an erroneous result so that I can add the noise before the slope is calculated or (2) have any ideas how to remove the erroneous result in the first place (to this aim, I have tried smoothing the matrix and scaling the diagonal amount other things with no luck).

I am using a mathematical function to estimate the covariance matrix for some process from the variances and then using this covariance matrix in a generalised least squares estimation of the gradient and intercept of the data.

However, sometimes I get nonsense values (i.e. 10e123, when the gradient should be ~1) for the gradient. Adding a small amount of Gaussian noise to the variances when this happens seems to remove the problem, but it is a bit inelegant of a solution. I have checked and there is no noticeable difference between the covariance matrix condition number/determinant/condition number when I get the erroneous result and not.

I was wondering if anyone knows how I either (1) detect when I will get an erroneous result so that I can add the noise before the gradient is calculated or (2) have any ideas how to remove the erroneous result in the first place (to this aim, I have tried smoothing the matrix and scaling the diagonal amount other things with no luck).

I am using a mathematical function to estimate the covariance matrix for some process from the variances and then using this covariance matrix in a generalised least squares estimation of the slope and intercept of the data.

However, sometimes I get nonsense values (i.e. 10e123, when the slope should be ~1) for the slope. Adding a small amount of Gaussian noise to the variances when this happens seems to remove the problem, but it is a bit inelegant of a solution. I have checked and there is no noticeable difference between the covariance matrix condition number/determinant/condition number when I get the erroneous result and not.

I was wondering if anyone knows how I either (1) detect when I will get an erroneous result so that I can add the noise before the slope is calculated or (2) have any ideas how to remove the erroneous result in the first place (to this aim, I have tried smoothing the matrix and scaling the diagonal amount other things with no luck).