A generalised linear model is considered in which the design variables may be functions of previous responses. Interest lies in estimating the parameters of the model. Approximations are derived for the bias and variance of the maximum likelihood estimators of the parameters. The derivations involve differentiating the fundamental identity of sequential analysis. The normal linear regression model, the logistic regression model and the dilution-series model are used to illustrate the approximations.
Bias calculations for adaptive generalised linear models
D. S. Coad, QMUL
Thu, 30/04/2015 - 16:45
Building 58 Room 4121 at the University of Southampton