Re: Dble exponential error model

From: LSheiner <lewis@c255.ucsf.edu> Subject: Re: Dble exponential error model Date: Thu, 07 Jan 1999 10:24:14 -0800 See my previous note regarding the original query; In answer to Steve's comments, see below ... Steve Duffull wrote: > > Hi Laurent > > > I need to calculate the Individual Weight residuals (IWRES) with a double > > exponential intra-individual error model. > > It would seem to me that you should obtain the weighting the same as usual. > Therefore > > W=F*EXP(EPS(1)) > > the rest of the error term "+THETA(10)*EXP(EPS(2))" is independent > of the model predicted concentration and is constant for any given > individual. While it may be constant, it none-the-less influences the variance. The model (corrected as in my last note, so as to linearize in EPS) says that the variance of an observation, plotted against the squared (true) value of the observation is a straight line with a non-zero intercept. The model above says that this regression goes through the origin. These are different variance models with quite different consequences for the relative weight assigned to different observations. > > However I have 2 points: > I do not understand why you would want to use this model. Steve may be refering to his next point, below, in which case I agree, but just in case there is a question in anyone's mind about the usefulness of the (corrected) model, Y = F + F*EPS(1) + EPS(2), let me opine that, as corrected, the model is perhaps the most useful single model for residual error that I know of: it says that there is a proportional component of error, but also a fixed component, for observations near zero, such that those observations are not associated with large weights, as they would be using a pure proportional (or power) error model. > I do not understand how your model can identify the difference > between THETA(10) and EPS(2). Actually, THETA(1) and SIGMA(2,2); this was the first point in my last note. LBS.