Robust Inference Based on Quasi-likelihoods for Generalized Linear Models and Longitudinal Data

E. Cantoni ()
Additional contact information
E. Cantoni: University of Geneva, Econometrics Department

A chapter in Developments in Robust Statistics, 2003, pp 114-124 from Springer

Abstract: Summary In this paper we introduce and develop robust versions of quasi-likelihood functions for model selection via an analysis-of-deviance type of procedure in generalized linear models and longitudinal data analysis. These robust functions are built upon natural classes of robust estimators and can be seen as weighted versions of their classical counterparts. The asymptotic theory of these test statistics is studied and their robustness properties are assessed for both generalized linear models and longitudinal data analysis. The proposed class of test statistics yields reliable inference even under model contamination. The analysis of a real data set completes the article.

Keywords: Robust inference; Quasi-likelihood functions; Estimating equations; Generalized linear models; Longitudinal data (search for similar items in EconPapers)
Date: 2003
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-3-642-57338-5_9

Ordering information: This item can be ordered from
http://www.springer.com/9783642573385

DOI: 10.1007/978-3-642-57338-5_9

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().