Efficient estimation of generalized linear latent variable models

Jenni Niku*, Wesley Brooks, Riki Herliansyah, Francis K.C. Hui, Sara Taskinen, David I. Warton

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    40 Citations (Scopus)

    Abstract

    Generalized linear latent variable models (GLLVM) are popular tools for modeling multivariate, correlated responses. Such data are often encountered, for instance, in ecological studies, where presence-absences, counts, or biomass of interacting species are collected from a set of sites. Until very recently, the main challenge in fitting GLLVMs has been the lack of computationally efficient estimation methods. For likelihood based estimation, several closed form approximations for the marginal likelihood of GLLVMs have been proposed, but their efficient implementations have been lacking in the literature. To fill this gap, we show in this paper how to obtain computationally convenient estimation algorithms based on a combination of either the Laplace approximation method or variational approximation method, and automatic optimization techniques implemented in R software. An extensive set of simulation studies is used to assess the performances of different methods, from which it is shown that the variational approximation method used in conjunction with automatic optimization offers a powerful tool for estimation.

    Original languageEnglish
    Article numbere0216129
    JournalPLoS ONE
    Volume14
    Issue number5
    DOIs
    Publication statusPublished - May 2019

    Fingerprint

    Dive into the research topics of 'Efficient estimation of generalized linear latent variable models'. Together they form a unique fingerprint.

    Cite this