Nonparametric modeling of clustered customer survival data

Iris Ivy M. Gauran, Erniel B. Barrios*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We incorporate a random clustering effect into the nonparametric version of Cox Proportional Hazards model to characterize clustered survival data. The simulation studies provide evidence that clustered survival data can be better characterized through a nonparametric model. Predictive accuracy of the nonparametric model is affected by number of clusters and distribution of the random component accounting for clustering effect. As the functional form of the covariate departs from linearity, the nonparametric model is becoming more advantageous over the parametric counterpart. Finally, nonparametric is better than parametric model when data are highly heterogenous and/or there is misspecification error.

Original languageEnglish (US)
Pages (from-to)603-618
Number of pages16
JournalCommunications in Statistics: Simulation and Computation
Issue number1
StatePublished - Jan 2 2017

Bibliographical note

Publisher Copyright:
© 2017 Taylor & Francis Group, LLC.


  • Backfitting algorithm
  • Clustered data
  • Generalized additive models
  • Nonparametric regression
  • Random effects
  • Survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation


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