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Variational Approximations for Generalized Linear Latent Variable Models

2017

Generalized linear latent variable models (GLLVMs) are a powerful class of models for understanding the relationships among multiple, correlated responses. Estimation, however, presents a major challenge, as the marginal likelihood does not possess a closed form for nonnormal responses. We propose a variational approximation (VA) method for estimating GLLVMs. For the common cases of binary, ordinal, and overdispersed count data, we derive fully closed-form approximations to the marginal log-likelihood function in each case. Compared to other methods such as the expectation-maximization algorithm, estimation using VA is fast and straightforward to implement. Predictions of the latent variabl…

0106 biological sciencesStatistics and ProbabilityMathematical optimizationBinary numberfactor analysisLatent variableordination010603 evolutionary biology01 natural sciences010104 statistics & probabilityItem response theoryDiscrete Mathematics and CombinatoricsApplied mathematicslatent trait0101 mathematicsLatent variable modelMathematicsta112item response theoryFunction (mathematics)Latent class modelMarginal likelihoodfaktorianalyysipappisvihkimysmultivariate analysisvariational approximationStatistics Probability and UncertaintyCount data
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