title: Model Comparison Tests to Determine Data Information Content.
abstract: In the context of inverse or parameter estimation problems we demonstrate the use of statistically based model comparison tests in several examples of practical interest. In these examples we are interested in questions related to information content of a particular given data set and whether the data will support a more complicated model to describe it. In the first example we compare fits for several different models to describe simple decay in a size histogram for aggregates in amyloid fibril formation. In a second example we investigate whether the information content in data sets for the pestLygus hesperusin cotton fields as it is currently collected is sufficient to support a model in which one distinguishes between nymphs and adults. Finally in a third example with data for patients having undergone an organ transplant, we question whether the data content is sufficient to estimate more than 5 of the fundamental parameters in a particular dynamic model.
authors.affiliation: Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212 USA.
authors.forename: H T
authors.initials: HT
authors.lastname: Banks
authors.affiliation: Division of Sciences & Mathematics, School of Interdisciplinary Arts & Sciences, University of Washington, Tacoma, Tacoma, Washington 98402.
authors.forename: J E
authors.initials: JE
authors.lastname: Banks
authors.affiliation: Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212 USA.
authors.forename: Kathryn
authors.initials: K
authors.lastname: Link
authors.affiliation: Department of Entomology and Nematology, and Center for Population Biology, University of California, Davis, Davis, CA 95616.
authors.forename: J A
authors.initials: JA
authors.lastname: Rosenheim
authors.affiliation: Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212 USA.
authors.forename: Chelsea
authors.initials: C
authors.lastname: Ross
authors.affiliation: Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212 USA.
authors.forename: K A
authors.initials: KA
authors.lastname: Tillman
keywords: Ordinary least squares; information content; model comparison in inverse problems