Robust Designs in Generalized Linear Models: A Quantile Dispersion Graphs Approach
This article studies design selection for generalized linear models (GLMs) using the quantile dispersion graphs (QDGs) approach in the presence of misspecification in the link and/or linear predictor. The uncertainty in the linear predictor is represented by a unknown function and estimated using kriging. For addressing misspecified link functions, a generalized family of link functions is used. Numerical examples are shown to illustrate the proposed methodology.
Communications in Statistics: Simulation and Computation
Das, I., Aggarwal, M., & Mukhopadhyay, S. (2015). Robust Designs in Generalized Linear Models: A Quantile Dispersion Graphs Approach. Communications in Statistics: Simulation and Computation, 44 (9), 2348-2370. https://doi.org/10.1080/03610918.2014.904343