Local Robust Estimation of Pareto-Type Tails with Random Right Censoring

Goedele Dierckx, Yuri Goegebeur*, Armelle Guillou

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


We propose a nonparametric robust estimator for the tail index of a conditional Pareto-type distribution in the presence of censoring and random covariates. The censored distribution is also of Pareto-type and the index is estimated locally within a narrow neighbourhood of the point of interest in the covariate space using the minimum density power divergence method. The main asymptotic properties of our robust estimator are derived under mild regularity conditions and its finite sample performance is illustrated on a small simulation study. A real data example is included to illustrate the practical applicability of the estimator.

Original languageEnglish
JournalSankhya A
Number of pages39
Publication statusE-pub ahead of print - 4. Jun 2019



  • 62G05
  • 62G20
  • 62G32
  • 62G35
  • Density power divergence
  • Local estimation
  • Pareto-type distribution
  • Random censoring

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