Analysis of experiments using the asymptotic quasi-likelihood approach

Sifa Mvoi and Yan-Xia Lin

Journal of Applied Statistics, 2003, vol. 30, issue 5, 485-505

Abstract: Comparison of treatment effects in an experiment is usually done through analysis of variance under the assumption that the errors are normally and independently distributed with zero mean and constant variance. The traditional approach in dealing with non-constant variance is to apply a variance stabilizing transformation and then run the analysis on the transformed data. In this approach, the conclusions of analysis of variance apply only to the transformed population. In this paper, the asymptotic quasi-likelihood method is introduced to the analysis of experimental designs. The weak assumptions of the asymptotic quasi-likelihood method make it possible to draw conclusions on heterogeneous populations without transforming them. This paper demonstrates how to apply the asymptotic quasi-likelihood technique to three commonly used models. This gives a possible way to analyse data given a complex experimental design.

Date: 2003
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/0266476032000053646 (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:30:y:2003:i:5:p:485-505

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

DOI: 10.1080/0266476032000053646

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 ().