Woods, D.C. and S.M. Lewis. "Continuous optimal designs for generalized linear models under model uncertainty." Journal of Statistical Theory and Practice. 5 (2011): 137-145. Electronic.
With more and more complex data and experiments, linear regression is often "inadequate." Even with the "modern" regression methods such as b-splines and smoothing kernels, standard factorial designs cannot be modeled well. This papers suggests are more exact designs for specific experiments using sophisticated design selection and criterion to allow uncertainty in the link (or knot) functions. The algorithm's efficiency is tested using simulation studies.
Again, this article helps me narrow down the statistics portion of my project. Understanding previous papers written by Dr. Woods helps me create a more solid literature review, helping me to develop my topic-specific information and significance.
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