Explicit estimating equations for semiparametric generalized linear latent variable models

Handle URI:
http://hdl.handle.net/10754/598287
Title:
Explicit estimating equations for semiparametric generalized linear latent variable models
Authors:
Ma, Yanyuan; Genton, Marc G.
Abstract:
We study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the semiparametric estimating equations. We further show that the explicit estimators have the usual root n consistency and asymptotic normality. We explain the computational implementation of our method and illustrate the numerical performance of the estimators in finite sample situations via extensive simulation studies. The advantage of our estimators over the existing likelihood approach is also shown via numerical comparison. We employ the method to analyse a real data example from economics. © 2010 Royal Statistical Society.
Citation:
Ma Y, Genton MG (2010) Explicit estimating equations for semiparametric generalized linear latent variable models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72: 475–495. Available: http://dx.doi.org/10.1111/j.1467-9868.2010.00741.x.
Publisher:
Wiley-Blackwell
Journal:
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
KAUST Grant Number:
KUSC1-016-04
Issue Date:
5-Jul-2010
DOI:
10.1111/j.1467-9868.2010.00741.x
Type:
Article
ISSN:
1369-7412
Sponsors:
We thank Maria-Pia Victoria-Feser for providing the Swiss consumption data. Ma's research was partially supported by National Science Foundation grant DMS-0906341. Genton's research was partially supported by National Science Foundation grants DMS-0504896 and CMG ATM-0620624, and award KUSC1-016-04 made by King Abdullah University of Science and Technology.
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Full metadata record

DC FieldValue Language
dc.contributor.authorMa, Yanyuanen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2016-02-25T13:18:01Zen
dc.date.available2016-02-25T13:18:01Zen
dc.date.issued2010-07-05en
dc.identifier.citationMa Y, Genton MG (2010) Explicit estimating equations for semiparametric generalized linear latent variable models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72: 475–495. Available: http://dx.doi.org/10.1111/j.1467-9868.2010.00741.x.en
dc.identifier.issn1369-7412en
dc.identifier.doi10.1111/j.1467-9868.2010.00741.xen
dc.identifier.urihttp://hdl.handle.net/10754/598287en
dc.description.abstractWe study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the semiparametric estimating equations. We further show that the explicit estimators have the usual root n consistency and asymptotic normality. We explain the computational implementation of our method and illustrate the numerical performance of the estimators in finite sample situations via extensive simulation studies. The advantage of our estimators over the existing likelihood approach is also shown via numerical comparison. We employ the method to analyse a real data example from economics. © 2010 Royal Statistical Society.en
dc.description.sponsorshipWe thank Maria-Pia Victoria-Feser for providing the Swiss consumption data. Ma's research was partially supported by National Science Foundation grant DMS-0906341. Genton's research was partially supported by National Science Foundation grants DMS-0504896 and CMG ATM-0620624, and award KUSC1-016-04 made by King Abdullah University of Science and Technology.en
dc.publisherWiley-Blackwellen
dc.subjectComplete statisticen
dc.subjectEstimation efficiencyen
dc.subjectLatent variableen
dc.subjectMaximum likelihood estimatoren
dc.subjectQuadrature pointsen
dc.subjectRobustnessen
dc.subjectScore functionen
dc.subjectSufficient statisticen
dc.titleExplicit estimating equations for semiparametric generalized linear latent variable modelsen
dc.typeArticleen
dc.identifier.journalJournal of the Royal Statistical Society: Series B (Statistical Methodology)en
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUSC1-016-04en
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