Sampling bias in logistic models

Add to your list(s) Download to your calendar using vCal

• Peter McCullagh (University of Chicago)
• Friday 14 November 2008, 16:00-17:00
• MR12, CMS, Wilberforce Road, Cambridge, CB3 0WB.

If you have a question about this talk, please contact rbg24.

This talk has been canceled/deleted

This talk is concerned with regression models for the effect of covariates on correlated binary and correlated polytomous responses. In a generalized linear mixed model, correlations are induced by a random effect, additive on the logistic scale, so that the joint distribution p_x(y) obtained by integration depends on the covariate values x on the sampled units. The thrust of this talk is that the conventional formulation is inappropriate for most natural sampling schemes in which the sampled units inevitably arise from a random process. The conventional analysis incorrectly predicts parameter attenuation due to the random effect, thereby giving a misleading impression of the magnitude of treatment effects. The error in the conventional analysis is a subtle consequence of sampling bias that arises from random sampling of units. This talk will describe a non-standard but mathematically natural formulation in which the units are auto-generated by an explicit sampling plan. For a quota sample in which the x-configuration is pre-specified, the model distribution coincides with p_x(y) in the GLMM . However, if the sample units are selected at random, for example by simple random sampling from the available population, the conditional distribution p(y | x)$ is different from p_x(y). By contrast with conventional models, conditioning on x is not equivalent to stratification by x. The implications for likelihood computations and estimating equations will be discussed.

This talk is part of the Statistics series.

This talk is included in these lists:

This talk is not included in any other list

Note that ex-directory lists are not shown.