Estimation of generalized linear latent variable models
By Philippe Huber, Elvezio Ronchetti, Maria-Pia Victoria-Feser. 2004
127 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/a868fcd267e46dae6e67163edbc1ea6c1f4d5a3e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- R. O’Hara
- F. Guillaume Blanchet
- Otso Ovaskainen
- Sara Taskinen
- Francis K. C. Hui
- David I. Warton
- G. Sileshi
- Jing Lei
In Statistics and Probability
- R. O’Hara
- F. Guillaume Blanchet
- Steve C. Walker
- Otso Ovaskainen
- Sara Taskinen
- Francis K. C. Hui
- David I. Warton
- Anthony R. Ives
Citations in the corpus
- Item Response Theory—A Statistical Framework for Educational and Psychological Measurement (2021, unknown): “…for integrals, log L ML (ˆ π 1 , ..., ˆ π J , ˆ f ) can be approximated by log L JL (ˆ π 1 , ..., ˆ π J , ˆ θ 1 , ..., ˆ θ N ) plus some smaller-order terms, where ˆ π j is the same MML estimator and ˆ θ i is given by ˆ θ i = arg max See Huber et al. (2004) for more details about this expansion.”
- Variational Approximations for Generalized Linear Latent Variable Models (2017, unknown): “These include Laplace’s approximation (Huber et al., 2004; Bianconcini and Cagnone, 2012), numerical integration methods such as adaptive quadrature (Cagnone and Monari, 2013), and the expectation-maximization (EM) algorithm or some variant of it (Sammel et al., 1997; Capp´e and Moulines, 2009);…”
- Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB (2024, unknown): “Substantially faster fits can be obtained using a Laplace (Huber, Ronchetti, and Victoria-Feser 2004; Niku, Warton, Hui, and Taskinen 2017) or variational approximation (Hui, Warton, Ormerod, Haapaniemi, and Taskinen 2017) to the marginal likelihood, as implemented in the R package gllvm (Niku et al”
- So Many Variables: Joint Modeling in Community Ecology. (2015, methodology): “The Laplace approximation is the most common maximum likelihood technique for estimating GLMMs and LVMs (Table I) – it makes simplifying assumptions that lead to a relatively fast estimation process, but which can be inaccurate in small samples [80].”