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.
|Keywords||Binary variables, Laplace approximation, Longitudinal data, M-estimators, Numerical integration, Random effects|
|Persistent URL||dx.doi.org/10.1214/17-EJS1360, hdl.handle.net/1765/103236|
|Journal||Electronic Journal of Statistics|
Bianconcini, S. (Silvia), Cagnone, S. (Silvia), & Rizopoulos, D. (2017). Approximate likelihood inference in generalized linear latent variable models based on the dimension-wise quadrature. Electronic Journal of Statistics, 11(2), 4404–4423. doi:10.1214/17-EJS1360