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2017 Approximate likelihood inference in generalized linear latent variable models based on the dimension-wise quadrature
Silvia Bianconcini, Silvia Cagnone, Dimitris Rizopoulos
Electron. J. Statist. 11(2): 4404-4423 (2017). DOI: 10.1214/17-EJS1360

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 proposed approximation outperforms several existing methods in simulations, and it can be successfully applied in presence of multidimensional longitudinal data when standard techniques are not applicable or feasible.

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Silvia Bianconcini. Silvia Cagnone. Dimitris Rizopoulos. "Approximate likelihood inference in generalized linear latent variable models based on the dimension-wise quadrature." Electron. J. Statist. 11 (2) 4404 - 4423, 2017. https://doi.org/10.1214/17-EJS1360

Information

Received: 1 September 2017; Published: 2017
First available in Project Euclid: 16 November 2017

zbMATH: 06816620
MathSciNet: MR3724224
Digital Object Identifier: 10.1214/17-EJS1360

Subjects:
Primary: 62F12, 62H25, 62P10, 62P25

JOURNAL ARTICLE
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Vol.11 • No. 2 • 2017
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