Variance Estimation for Statistics Computed from Single Recurrent Event Processes

Yongtao Guan, Jun Yan, Rajita Sinha

Research output: Contribution to journalArticlepeer-review


This article is concerned with variance estimation for statistics that are computed from single recurrent event processes. Such statistics are important in diagnosis for each individual recurrent event process. The proposed method only assumes a semiparametric form for the first-order structure of the processes but not for the second-order (i.e., dependence) structure. The new variance estimator is shown to be consistent for the target parameter under very mild conditions. The estimator can be used in many applications in semiparametric rate regression analysis of recurrent event data such as outlier detection, residual diagnosis, as well as robust regression. A simulation study and application to two real data examples are used to demonstrate the use of the proposed method.

Original languageEnglish (US)
Pages (from-to)711-718
Number of pages8
Issue number3
StatePublished - Sep 2011
Externally publishedYes


  • Inhomogeneous point process
  • Recurrent event process
  • Robust regression
  • Variance estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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