A0161
Title: Practical design strategies for similar compound interaction modeling
Authors: Timothy OBrien - Loyola University Chicago (United States) [presenting]
Abstract: Researchers often find that generalized linear or nonlinear regression models are applicable for scientific assessment, including physical, agricultural, environmental, and synergy modeling. In line with other processes, these nonlinear models perform better than linear models since they tend to fit the data well, and the associated model parameter(s) are typically scientifically meaningful. Generalized nonlinear model parameter estimation presents statistical modeling challenges, including computational, convergence, and curvature issues. Further, researchers are also often in a position of requiring optimal or near-optimal (so-called robust) designs for the given chosen nonlinear model. A common shortcoming of most optimal designs for nonlinear models used in practical settings, however, is that these designs typically focus only on (first-order) parameter variance or predicted variance and thus ignore the inherent nonlinearity of the assumed model function. Another shortcoming of optimal designs is that they often have only p support points, where p is the number of model parameters. The aim is to examine modeling and estimation methods connected with generalized nonlinear models, which are useful for physical, environmental, and biological modeling, and to provide concrete, novel suggestions related to robust design strategies for these models. Numerous examples are provided, and software methods are discussed.
