RE: WRES AND OUTLIER IDENTIFICATION/EXCLUSION

From: "Kowalski, Ken" Ken.Kowalski@pfizer.com Subject: RE: [NMusers] WRES AND OUTLIER IDENTIFICATION/EXCLUSION Date: Mon, 2 Oct 2006 16:21:25 -0400 Mats, Nice suggestion. By incorporating an interindividual mixture model for subjects with at least one outlier we can output MIXEST for each individual as a diagnostic. This would allow us to identify which individuals have at least one outlier observation (and which individuals are "clean" of any outliers). Still, within an individual in the outlier population we wouldn't know which observations are being classified as outliers vs non-outliers without further post-processing of the individual observation contributions to the likelihood (don't ask me how to do this :) ). While this is an interesting approach (our original proposal with or without your interindividual mixture model suggestion) it is not clear to me how well this approach will work in practice. I suspect that any misspecification of the structural as well as statistical models may result in the estimation of subpopulations that may not exactly coincide with outlier vs non-outlier populations of observations. I would probably start with the two-stage approach and go to a full likelihood approach if one shows that the subpopulations coincide with the outlier vs non-outlier dichotomy based on a pre-specified criteria for outliers. If the outliers tend to have more positive rather than negative residuals or if they are not randomly distributed about the curve, this might be an indication of model misspecification which may have to be resolved before entertaining this full likelihood outlier approach and what form of contaminated residual error model to employ. Best regards, Ken