Semiparametric efficient and robust estimation of an unknown symmetric population under arbitrary sample selection bias

Yanyuan Ma, Mijeong Kim, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

We propose semiparametric methods to estimate the center and shape of a symmetric population when a representative sample of the population is unavailable due to selection bias. We allow an arbitrary sample selection mechanism determined by the data collection procedure, and we do not impose any parametric form on the population distribution. Under this general framework, we construct a family of consistent estimators of the center that is robust to population model misspecification, and we identify the efficient member that reaches the minimum possible estimation variance. The asymptotic properties and finite sample performance of the estimation and inference procedures are illustrated through theoretical analysis and simulations. A data example is also provided to illustrate the usefulness of the methods in practice.

Original languageEnglish (US)
Pages (from-to)1090-1104
Number of pages15
JournalJournal of the American Statistical Association
Volume108
Issue number503
DOIs
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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