A semiparametric estimation procedure of dependence parameters in multivariate families of distributions

Clicks: 7
ID: 296546
1995
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 #83 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 investigates the properties of a semiparametric method for estimating the dependence parameters in a family of multivariate distributions. The proposed estimator, obtained as a solution of a pseudo-likelihood equation, is shown to be consistent, asymptotically normal and fully efficient at independence. A natural estimator of its asymptotic variance is proved to be consistent. Comparisons are made with alternative semiparametric estimators in the special case of Clayton's model for association in bivariate data.
Reference Key
openalex_W1990860515 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Christian Genest, Kilani Ghoudi, Louis‐Paul Rivest
Journal jurnal biometrika dan kependudukan
Year 1995
DOI
10.1093/biomet/82.3.543
URL
https://doi.org/10.1093/biomet/82.3.543
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.