Multivariate Data and GLM: Generalized Estimating Equations

M. Ataharul Islam () and Soma Chowdhury Biswas ()
Additional contact information
M. Ataharul Islam: University of Dhaka, ISRT
Soma Chowdhury Biswas: University of Chittagong, Department of Statistics

Chapter Chapter 6 in Generalized Linear Models and Extensions, 2025, pp 97-120 from Springer

Abstract: Abstract Generalized linear models (GLMs) have been extended to handle multivariate data arising from longitudinal or repeated measures using various approaches: Quasi-likelihood, working likelihood, and pseudo-likelihood methods play a crucial role. Among these, quasi-likelihood is commonly used for analyzing longitudinal data. The generalized estimating equations (GEE) approach, a quasi-likelihood method, addresses correlated responses over time. Researchers have proposed alternative procedures to enhance the analysis of multivariate data in this context. Few of these methods are discussed in this chapter.

Date: 2025
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:978-981-96-4726-2_6

Ordering information: This item can be ordered from
http://www.springer.com/9789819647262

DOI: 10.1007/978-981-96-4726-2_6

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 ().