Details

Autor(en) / Beteiligte

• Huber, Philippe
• Ronchetti, Elvezio
• Victoria-Feser, Maria-Pia

Titel

Estimation of generalized linear latent variable models

Ist Teil von

• Journal of the Royal Statistical Society. Series B, Statistical methodology, 2004-11, Vol.66 (4), p.893-908

Ort / Verlag

Oxford, UK: Blackwell Publishing

Erscheinungsjahr

2004

Link zum Volltext

Quelle

Business Source Ultimate

Beschreibungen/Notizen

• Generalized linear latent variable models (GLLVMs), as defined by Bartholomew and Knott, enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the complexity of the log-likelihood function of a GLLVM, an approximation such as numerical integration must be used for inference. This can limit drastically the number of variables in the model and can lead to biased estimators. We propose a new estimator for the parameters of a GLLVM, based on a Laplace approximation to the likelihood function and which can be computed even for models with a large number of variables. The new estimator can be viewed as an M-estimator, leading to readily available asymptotic properties and correct inference. A simulation study shows its excellent finite sample properties, in particular when compared with a well-established approach such as LISREL. A real data example on the measurement of wealth for the computation of multidimensional inequality is analysed to highlight the importance of the methodology.