Approximate likelihood inference in generalized linear latent variable models based on the dimension-wise quadrature
Abstract
We propose a new method to perform approximate likelihood inference in latent variable models. Our approach provides an approximation of the integrals involved in the likelihood function through a reduction of their dimension that makes the computation feasible in situations in which classical and adaptive quadrature based methods are not applicable. We derive new theoretical results on the accuracy of the obtained estimators. We show that the...
Paper Details
Title
Approximate likelihood inference in generalized linear latent variable models based on the dimension-wise quadrature
Published Date
Jan 1, 2017
Volume
11
Issue
2
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