An Inverse Problem Statistical Methodology Summary

H. Thomas Banks (), Marie Davidian (), John R. Samuels () and Karyn L. Sutton ()
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H. Thomas Banks: North Carolina State University, Center for Research in Scientific Computation and Center for Quantitative Sciences in Biomedicine
Marie Davidian: North Carolina State University, Center for Research in Scientific Computation and Center for Quantitative Sciences in Biomedicine
John R. Samuels: North Carolina State University, Center for Research in Scientific Computation and Center for Quantitative Sciences in Biomedicine
Karyn L. Sutton: North Carolina State University, Center for Research in Scientific Computation and Center for Quantitative Sciences in Biomedicine

A chapter in Mathematical and Statistical Estimation Approaches in Epidemiology, 2009, pp 249-302 from Springer

Abstract: Abstract We discuss statistical and computational aspects of inverse or parameter estimation problems for deterministic dynamical systems based on Ordinary Least Squares and Generalized Least Squares with appropriate corresponding data noise assumptions of constant variance and nonconstant variance (relative error), respectively. Among the topics included here are mathematical model, statistical model and data assumptions, and some techniques (residual plots, sensitivity analysis, model comparison tests) for verifying these. The ideas are illustrated throughout with the popular logistic growth model of Verhulst and Pearl as well as with a recently developed population level model of pneumococcal disease spread.

Keywords: Inference; Least squares inverse problems; Parameter estimation; Sensitivity and generalized sensitivity functions (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-90-481-2313-1_11

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DOI: 10.1007/978-90-481-2313-1_11

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