Reparameterization of nonlinear statistical models: a case study

Gavin Ross and C. Sarada

Journal of Applied Statistics, 2010, vol. 37, issue 12, 2015-2026

Abstract: The importance of finding appropriate parameterizations for nonlinear statistical models is highlighted. The purpose of this paper is to explore the principles of reparameterization, using an example from real data. It is shown that stable parameterizations allow likelihood-based confidence intervals to be computed. Further, it is noted that the choice of error distribution may seriously affect the estimates and confidence intervals of quantities of interest. The influence of each observation on the estimation of each parameter is displayed for each error model. Multidimensional likelihood contours may be displayed pairwise using profile likelihood computations.

Keywords: aphid population growth model; normal distributed errors; Poisson distributed errors; profile likelihoods; reparameterization; stable parameters (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760903207332 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:37:y:2010:i:12:p:2015-2026

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760903207332

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().