Nonparametric regression with filtered data

Oliver Linton, Enno Mammen, Jens Perch Nielsen, Ingrid Van Keilegom

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)

Abstract

We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.

Original languageEnglish
Pages (from-to)60-87
Number of pages28
JournalBernoulli
Volume17
Issue number1
DOIs
Publication statusPublished - Feb 2011
Externally publishedYes

Keywords

  • Censoring
  • Counting process theory
  • Hazard functions
  • Kernel estimation
  • Local linear estimation
  • Truncation

Cite this