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Explicit estimating equations for semiparametric generalized linear latent variable models
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DOI：10.1111/j.1467-9868.2010.00741.x.png)
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摘要
En 中文
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.
Keyword:
Complete statistic
Estimation efficiency
Latent variable
Maximum likelihood estimator
Quadrature points
Robustness
Score function
Sufficient statistic
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3.6
论文数:
1.5K
被引数:
3.2W
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