Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 11, Number 2 (2017), 4404-4423.
Approximate likelihood inference in generalized linear latent variable models based on the dimension-wise quadrature
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.
Electron. J. Statist., Volume 11, Number 2 (2017), 4404-4423.
Received: September 2017
First available in Project Euclid: 16 November 2017
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62H25: Factor analysis and principal components; correspondence analysis 62F12: Asymptotic properties of estimators 62P10: Applications to biology and medical sciences 62P25: Applications to social sciences
Bianconcini, Silvia; Cagnone, Silvia; Rizopoulos, Dimitris. Approximate likelihood inference in generalized linear latent variable models based on the dimension-wise quadrature. Electron. J. Statist. 11 (2017), no. 2, 4404--4423. doi:10.1214/17-EJS1360. https://projecteuclid.org/euclid.ejs/1510801791