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

Yanyuan Ma; Mijeong Kim; Marc G. Genton

doi:10.1080/01621459.2013.816184

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

Yanyuan Ma, Mijeong Kim, Marc G. Genton

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6 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. © 2013 American Statistical Association.

Original language	English (US)
Pages (from-to)	1090-1104
Number of pages	15
Journal	Journal of the American Statistical Association
Volume	108
Issue number	503
DOIs	https://doi.org/10.1080/01621459.2013.816184
State	Published - Sep 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: 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).

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1080/01621459.2013.816184

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title = "Semiparametric efficient and robust estimation of an unknown symmetric population under arbitrary sample selection bias",

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. {\textcopyright} 2013 American Statistical Association.",

author = "Yanyuan Ma and Mijeong Kim and Genton, {Marc G.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: 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).",

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AU - Ma, Yanyuan

AU - Kim, Mijeong

AU - Genton, Marc G.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: 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).

PY - 2013/9

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N2 - 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.

AB - 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.

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UR - http://www.tandfonline.com/doi/abs/10.1080/01621459.2013.816184

UR - http://www.scopus.com/inward/record.url?scp=84890083072&partnerID=8YFLogxK

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DO - 10.1080/01621459.2013.816184

M3 - Article

SN - 0162-1459

VL - 108

SP - 1090

EP - 1104

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

IS - 503

ER -

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

Abstract

Bibliographical note

ASJC Scopus subject areas

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