Order-restricted semiparametric inference for the power bias model

Ori Davidov, Konstantinos Fokianos, George Iliopoulos

Research output: Contribution to journalArticlepeer-review

Abstract

The power bias model, a generalization of length-biased sampling, is introduced and investigated in detail. In particular, attention is focused on order-restricted inference. We show that the power bias model is an example of the density ratio model, or in other words, it is a semiparametric model that is specified by assuming that the ratio of several unknown probability density functions has a parametric form. Estimation and testing procedures under constraints are developed in detail. It is shown that the power bias model can be used for testing for, or against, the likelihood ratio ordering among multiple populations without resorting to any parametric assumptions. Examples and real data analysis demonstrate the usefulness of this approach.

Original languageEnglish
Pages (from-to)549-557
Number of pages9
JournalBiometrics
Volume66
Issue number2
DOIs
StatePublished - Jun 2010

Keywords

  • Biased sampling
  • Empirical likelihood
  • Likelihood ratio order
  • Pool adjacent violators algorithm (PAVA)
  • Semiparametric models
  • Usual stochastic order

ASJC Scopus subject areas

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

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