Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method

Clicks: 3
ID: 291194
1974
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 #159 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
To define a likelihood we have to specify the form of distribution of the observations, but to define a quasi-likelihood function we need only specify a relation between the mean and variance of the observations and the quasi-likelihood can then be used for estimation. For a one-parameter exponential family the log likelihood is the same as the quasi-likelihood and it follows that assuming a one-parameter exponential family is the weakest sort of distributional assumption that can be made. The Gauss-Newton method for calculating nonlinear least squares estimates generalizes easily to deal with maximum quasi-likelihood estimates, and a rearrangement of this produces a generalization of the method described by Nelder & Wedderburn (1972).
Reference Key
openalex_W1979159029 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors R. W. M. Wedderburn
Journal jurnal biometrika dan kependudukan
Year 1974
DOI
10.1093/biomet/61.3.439
URL
https://doi.org/10.1093/biomet/61.3.439
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.