5
Views
1
CrossRef citations to date
0
Altmetric
Original Articles

Corrected Confidence Levels for Adaptive Nonlinear Regression

Pages 79-93 | Published online: 14 Aug 2013
 

SYNOPTIC ABSTRACT

Consider a nonlinear regression model of the form yk = g(xk;θ)+ek, where θ is an unknown parameter, xk = xk (y1,…,yk-1 is a design variable which depends on previous repsonses for k = 1, 2, …, and e1, e2, … are normal errors. Then the functional form of the maximum likelihood estimator is the same as for deterministic designs, but the sampling distributions may be quite different. Asymptotic expansions for the sampling distributions of normalized estimation error are found, and their use in setting confidence bounds is illustrated. The expansions are unconventional in that a Bayesian approach is used to obtain frequentist properties.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.