Semiparametric Regression for the Mean and Rate Functions of Recurrent Events
Clicks: 3
ID: 301305
2000
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.
Reader Engagement
Emerging Content
0.6
/100
3 views
2 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #37 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 145 in total.
Mint this article as an NFT
Not yet mintedCreate 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
Abstract
Summary The counting process with the Cox-type intensity function has been commonly used to analyse recurrent event data. This model essentially assumes that the underlying counting process is a time-transformed Poisson process and that the covariates have multiplicative effects on the mean and rate function of the counting process. Recently, Pepe and Cai, and Lawless and co-workers have proposed semiparametric procedures for making inferences about the mean and rate function of the counting process without the Poisson-type assumption. In this paper, we provide a rigorous justification of such robust procedures through modern empirical process theory. Furthermore, we present an approach to constructing simultaneous confidence bands for the mean function and describe a class of graphical and numerical techniques for checking the adequacy of the fitted mean–rate model. The advantages of the robust procedures are demonstrated through simulation studies. An illustration with multiple-infection data taken from a clinical study on chronic granulomatous disease is also provided.
| Reference Key |
openalex_W2012053212
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | D. Y. Lin, L. J. Wei, Ilsoon Yang, Z. Ying |
| Journal | Journal of the Royal Statistical Society Series B (Statistical Methodology) |
| Year | 2000 |
| DOI |
10.1111/1467-9868.00259
|
| URL | |
| 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.