Semiparametric efficient and robust estimation of an unknown symmetric population under arbitrary sample selection bias
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Spatio-Temporal Statistics and Data Analysis Group
KAUST Grant NumberKUS-C1-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/562952
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AbstractWe 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. © 2013 American Statistical Association.
CitationMa, Y., Kim, M., & Genton, M. G. (2013). Semiparametric Efficient and Robust Estimation of an Unknown Symmetric Population Under Arbitrary Sample Selection Bias. Journal of the American Statistical Association, 108(503), 1090–1104. doi:10.1080/01621459.2013.816184
SponsorsThis research was partially supported by NSF grants DMS-0906341, DMS-1007504, and DMS-1100492; NINDS grant R01-NS073671; and by Award No. KUS-C1-016-04 made by King Abdullah University of Science and Technology (KAUST).
PublisherInforma UK Limited