Robust Inference in Conditionally Linear Nonlinear Regression Model
Editor(s)
Rootzen, Holger and Rudemo, Mats
Abstract
We consider robust methods of likelihood and frequentist inference for the nonlinear parameter, say α, in conditionally linear nonlinear regression models. We derive closed-form expressions for robust conditional, marginal, profile and modified profile likelihood functions for α under elliptically contoured data distributions. Next, we develop robust exact-F confidence intervals for α and consider robust Fieller intervals for ratios of regression parameters in linear models. Several well-known examples are considered and Monte Carlo simulation results are presented.
Recommended Citation
R. Paige and P. H. Fernando, "Robust Inference in Conditionally Linear Nonlinear Regression Model," Scandinavian Journal of Statistics, Wiley-Blackwell, Jan 2008.
The definitive version is available at https://doi.org/10.1111/j.1467-9469.2007.00570.x
Department(s)
Mathematics and Statistics
Keywords and Phrases
calibration; conditionally linear regression models; elliptically contoured models; parallel line assay; pseudo-likelihoods; robust likelihoods; slope-ratio assays
International Standard Serial Number (ISSN)
0303-6898
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2008 Wiley-Blackwell, All rights reserved.
Publication Date
01 Jan 2008
