I find the question itself to be puzzling -- so I have split it up and numbered the pieces.
On 19 Sep 2003 21:35:22 -0700, [EMAIL PROTECTED] (Anwar) wrote: "Does anyone know of regression methods that satisfy the following three criteria: 1) nonlinear, 2) variable coefficients (i.e. updating of coefficient values), AND 3) minimize out-of-sample, one-step ahead, forecasting error rather than in-sample fit? " ====== end of quote from the post. On (1). What sort of 'nonlinear' ? Engineers and psychologists often include simple polynomials when they say 'nonlinear', since these can draw curved lines; whereas statisticians are concerned with nonlinear-in-the- coefficients, which are more complicated. So, do you mean the easy nonlinear, or the tough one? On (2). Frankly, 'variable coefficients' practically freezes my brain: when the phrase is re-interpreted, so that it is not referring to "coefficients of variables" but rather "updating of coefficient values." Okay. Is this a matter of adapting robust lines to windows of certain widths? Or, could it be a request for adaptive-learning? Something else? For either of the first two, I think that calling it a computer implementation of < ... > would be better that calling it a 'regression method' but I'm prejudiced because I don't know what would be a single acceptable example, ignoring entirely the precepts of (1) and (3). On (3). Huh? It seems to me, this says nothing more than, "The model does seem to comprise a time-series." And someone wants prediction. Plus, they use a different vocabulary than what I would use. This isn't one more prospectus for outwitting the stock market, is it? -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html "Taxes are the price we pay for civilization." . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
