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

Handle URI:
http://hdl.handle.net/10754/562952
Title:
Semiparametric efficient and robust estimation of an unknown symmetric population under arbitrary sample selection bias
Authors:
Ma, Yanyuan; Kim, Mijeong; Genton, Marc G. ( 0000-0001-6467-2998 )
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. © 2013 American Statistical Association.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Spatio-Temporal Statistics and Data Analysis Group
Publisher:
Informa UK Limited
Journal:
Journal of the American Statistical Association
Issue Date:
Sep-2013
DOI:
10.1080/01621459.2013.816184
Type:
Article
ISSN:
01621459
Sponsors:
This 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).
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorMa, Yanyuanen
dc.contributor.authorKim, Mijeongen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2015-08-03T11:16:48Zen
dc.date.available2015-08-03T11:16:48Zen
dc.date.issued2013-09en
dc.identifier.issn01621459en
dc.identifier.doi10.1080/01621459.2013.816184en
dc.identifier.urihttp://hdl.handle.net/10754/562952en
dc.description.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.en
dc.description.sponsorshipThis 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).en
dc.publisherInforma UK Limiteden
dc.subjectEfficiencyen
dc.subjectNonrandom dataen
dc.subjectRobustnessen
dc.subjectSemiparametric modelen
dc.subjectSkewnessen
dc.subjectSymmetric distributionen
dc.titleSemiparametric efficient and robust estimation of an unknown symmetric population under arbitrary sample selection biasen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentSpatio-Temporal Statistics and Data Analysis Groupen
dc.identifier.journalJournal of the American Statistical Associationen
dc.contributor.institutionDepartment of Statistics, Texas A and M University, College Station, TX 77843-3143, United Statesen
kaust.authorGenton, Marc G.en
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