Longitudinal data analysis using generalized linear models

Clicks: 3
ID: 289118
1986
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #133 of 188 articles by views in jurnal biometrika dan kependudukan

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 188 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for niultivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m-dependence and exchangeable correlation structures from each subject are discussed. Efficiency of the pioposecl estimators in two simple situations is considered. The approach is closely related to quasi-likelihood.
Reference Key
openalex_W2149860264 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kung‐Yee Liang, Scott L. Zeger
Journal jurnal biometrika dan kependudukan
Year 1986
DOI
10.1093/biomet/73.1.13
URL
https://doi.org/10.1093/biomet/73.1.13
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.