Likelihood based geo-statistics (
geoR etc.) are usually slower than non-likelihood based geo-statistics (i.e. those based on just least square fitting, for example
It is slower in two respect: parameter estimation and the actual prediction exercise (kriging).
By 'slower' I really mean two things:
- It literally takes more time to perform those steps, and
- It can't handle large dataset.
What I don't mean is
geoR being slower than
gstat. I understand one is pure R whereas the other is in C, so they will naturally perform differently.
My questions is why is likelihood based method slower at both parameter estimation and prediction than non-likelihood based method?
What (I think) I know so far is that, in estimation, likelihood based method involves dealing with a NxN covariance matrix whereas non-likelihood based method doesn't have to deal with that. So that's time-saving.
But in prediction, I thought both models have to deal with the NxN covariance matrix (am I right?), so how can non-likelihood based method handle large dataset quicker than likelihood based methods?