> > Note that the "predictive ability" *is* affected in the sense that > a prediction interval for the next observation will not have the > correct coverage probability, because you have ignored the explanatory > effect of the immediately previous observations.
My working assumption is that the explanatory effect of any (originally) previous observation is irrelevant. The model does not need to take into account the value of previous observations. Given this situation, I would think that using time series techniques and methods would be inappropriate (even though I do collect the data sequentially over time). > > Note that your future observations still arrive /in order/, not > shuffled. It doesn't help you to pretend otherwise. The fact that future values may arrive in order is, again, irrelevant. I will use the model to predict a future value out of order (ie, in the same way oen would use any linear regression equation) so again, I do not think I should/could use time series methods. The dilemma is that time series methods seem inappropriate, but there are also violations of most of the assumptions for using std regression methods as well unless I take actions to offset these (for example, resorting the data). Maybe I need to go completely to non parametric methods... ? Steve . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
