title: Estimation in regression models with externally estimated parameters.
abstract: In many regression applications, some of the model parameters are estimated from separate data sources. Typically, these estimates are plugged into the regression model and the remainder of the parameters is estimated from the primary data source. This situation arises frequently in compartment modeling when there is an external input function to the system. This paper provides asymptotic and bootstrap-based approaches for accounting for all sources of variability when computing standard errors for estimated regression model parameters. Examples and simulations are provided to motivate and illustrate the ideas.
authors: Department of Biostatistics, Columbia University, 6th floor, 722 West 168th Street, New York, NY 10032, USA. to166@columbia.edu
authors: R Todd
authors: RT
authors: Ogden
authors: Thaddeus
authors: T
authors: Tarpey
mesh_terms: D000465:Algorithms; D001923:Brain Chemistry; D003198:Computer Simulation; D003625:Data Collection / Q000379:methods; D006801:Humans; D008954:Models, Biological; D049268:Positron-Emission Tomography; D012044:Regression Analysis; D015032:Zinc / Q000378:metabolism
chemical_list: D015032:Zinc