Explicit estimating equations for semiparametric generalized linear latent variable models
Yanyuan Ma and
Marc G. Genton
Journal of the Royal Statistical Society Series B, 2010, vol. 72, issue 4, 475-495
Abstract:
Summary. 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.
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
https://doi.org/10.1111/j.1467-9868.2010.00741.x
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssb:v:72:y:2010:i:4:p:475-495
Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9868
Access Statistics for this article
Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom
More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().