I have a question about the pic above.

Below is the original descriptions attached to this pic.

"For example, the root-mean-squared prediction error may be smaller for a particular model. Therefore, you might conclude that it is the optimal model. However, when comparing to another model, the root-mean-squared prediction error may be closer to the average estimated prediction standard error. This is a more valid model, because when you predict at a point without data, you have only the estimated standard errors to assess your uncertainty of that prediction. When the average estimated prediction standard errors are close to the root-mean-squared prediction errors from cross-validation, you can be confident that the prediction standard errors are appropriate. In the figure above, both kriging models are good, but those at the left are slightly better."

My question is, that, the ones with the root-mean-squared prediction errors closer to average standard errors should be the right one, instead of the left one. Why does it say that those at the left are slightly better? Thanks.

If nothing else, we shouldn't be calling them both kriging models because the model on the right is from Local Polynomial Interpolation, which isn't a kriging model.

Thanks for the feedback.