Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models
Abstract
Maximum likelihood estimation of models based on continuous latent variables generally requires to solve integrals that are not analytically tractable. Numerical approximations represent a possible solution to this problem. We propose to use the adaptive Gaussian---Hermite (AGH) numerical quadrature approximation for a particular class of continuous latent variable models for time-series and longitudinal data. These dynamic models are based on...
Paper Details
Title
Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models
Published Date
Apr 1, 2017
Volume
49
Issue
4
Pages
599 - 622
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