A necessary and sufficient condition for justifying non-parametric likelihood with censored data

Qiqing Yu, Yuting Hsu, Kai Yu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The non-parametric likelihood L(F) for censored data, including univariate or multivariate right-censored, doubly-censored, interval-censored, or masked competing risks data, is proposed by Peto (Appl Stat 22:86–91, 1973). It does not involve censoring distributions. In the literature, several noninformative conditions are proposed to justify L(F) so that the GMLE can be consistent (see, for examples, Self and Grossman in Biometrics 42:521–530 1986, or Oller et al. in Can J Stat 32:315–326, 2004). We present the necessary and sufficient (N&S) condition so that L(F) is equivalent to the full likelihood under the non-parametric set-up. The statement is false under the parametric set-up. Our condition is slightly different from the noninformative conditions in the literature. We present two applications to our cancer research data that satisfy the N&S condition but has dependent censoring.

Original languageEnglish (US)
Pages (from-to)995-1011
Number of pages17
JournalMetrika
Volume77
Issue number8
DOIs
StatePublished - Oct 14 2014

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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