Robust designs for misspecified logistic models

Merck Research Laboratories, North Wales, Pennsylvania 19454, United States; Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2G1
Journal of Statistical Planning and Inference (Impact Factor: 0.71). 01/2009; DOI: 10.1016/j.jspi.2008.05.022
Source: OAI

ABSTRACT We develop criteria that generate robust designs and use such criteria for the construction of designs that insure against possible misspecifications in logistic regression models. The design criteria we propose are different from the classical in that we do not focus on sampling error alone. Instead we use design criteria that account as well for error due to bias engendered by the model misspecification. Our robust designs optimize the average of a function of the sampling error and bias error over a specified misspecification neighbourhood. Examples of robust designs for logistic models are presented, including a case study implementing the methodologies using beetle mortality data.

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