Estimation in regression models with externally estimated parameters.
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.
Ogden
R Todd
RT
Department of Biostatistics, Columbia University, 6th floor, 722 West 168th Street, New York, NY 10032, USA. to166@columbia.edu
Tarpey
Thaddeus
T
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
D015032:Zinc