Bayesian latent variable models for hierarchical clustered count outcomes with repeated measures in microbiome studies
Sign Up to like & getrecommendations! Published in 2017 at "Genetic Epidemiology"
DOI: 10.1002/gepi.22031
Abstract: Motivated by the multivariate nature of microbiome data with hierarchical taxonomic clusters, counts that are often skewed and zero inflated, and repeated measures, we propose a Bayesian latent variable methodology to jointly model multiple operational… read more here.
Keywords: models hierarchical; variable models; bayesian latent; repeated measures ... See more keywords
Composite quantile regression estimation of linear error-in-variable models using instrumental variables
Sign Up to like & getrecommendations! Published in 2019 at "Metrika"
DOI: 10.1007/s00184-019-00734-5
Abstract: In this paper, we develop a composite quantile regression estimator of linear error-in-variable models based on instrumental variables. The proposed estimator is consistent and asymptotically normal under fairly general assumptions. It neither requires the measurement… read more here.
Keywords: regression; linear error; composite quantile; variable models ... See more keywords
Deviance information criterion for latent variable models and misspecified models
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Econometrics"
DOI: 10.1016/j.jeconom.2019.11.002
Abstract: Deviance information criterion (DIC) has been widely used for Bayesian model comparison, especially after Markov chain Monte Carlo (MCMC) is used to estimate candidate models. This paper first studies the problem of using DIC to… read more here.
Keywords: misspecified models; variable models; deviance information; information criterion ... See more keywords
Monitoring and prediction of big process data with deep latent variable models and parallel computing
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Process Control"
DOI: 10.1016/j.jprocont.2020.05.010
Abstract: Abstract Process monitoring and quality prediction are crucial for maintaining favorable operating conditions and have received considerable attention in previous decades. For majority complicated cases in chemical and biological industrial processes with particular nonlinear characteristics,… read more here.
Keywords: variable models; process; parallel computing; prediction ... See more keywords
Spatial interdependence and instrumental variable models
Sign Up to like & getrecommendations! Published in 2020 at "Political Science Research and Methods"
DOI: 10.1017/psrm.2018.61
Abstract: Abstract Instrumental variable (IV) methods are widely used to address endogeneity concerns. Yet, a specific kind of endogeneity – spatial interdependence – is regularly ignored. We show that ignoring spatial interdependence in the outcome results… read more here.
Keywords: spatial interdependence; interdependence instrumental; interdependence; instrumental variable ... See more keywords
Seeing the impossible: Visualizing latent variable models with flexplavaan.
Sign Up to like & getrecommendations! Published in 2022 at "Psychological methods"
DOI: 10.1037/met0000468
Abstract: Latent variable models (LVMs) are incredibly flexible tools that allow users to address research questions they might otherwise never be able to answer (McDonald, 2013). However, one major limitation of LVMs is evaluating model fit.… read more here.
Keywords: latent variable; model fit; model; seeing impossible ... See more keywords
The Thorny Relation Between Measurement Quality and Fit Index Cutoffs in Latent Variable Models
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Personality Assessment"
DOI: 10.1080/00223891.2017.1281286
Abstract: ABSTRACT Latent variable modeling is a popular and flexible statistical framework. Concomitant with fitting latent variable models is assessment of how well the theoretical model fits the observed data. Although firm cutoffs for these fit… read more here.
Keywords: quality fit; variable models; measurement; measurement quality ... See more keywords
Deep Learning of Latent Variable Models for Industrial Process Monitoring
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2021.3134251
Abstract: Data-driven process monitoring based on latent variable models are widely employed in industry. This article proposes a novel monitoring framework for latent variable models using hierarchical feature extraction, Bayesian inference, and weighting strategy. We first… read more here.
Keywords: variable models; process; latent variable; process monitoring ... See more keywords
Hidden variable models reveal the effects of infection from changes in host survival
Sign Up to like & getrecommendations! Published in 2023 at "PLOS Computational Biology"
DOI: 10.1371/journal.pcbi.1010910
Abstract: The impacts of disease on host vital rates can be demonstrated using longitudinal studies, but these studies can be expensive and logistically challenging. We examined the utility of hidden variable models to infer the individual… read more here.
Keywords: hidden variable; disease; models reveal; variable models ... See more keywords
Efficient estimation of generalized linear latent variable models
Sign Up to like & getrecommendations! Published in 2019 at "PLoS ONE"
DOI: 10.1371/journal.pone.0216129
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… read more here.
Keywords: estimation; variable models; generalized linear; efficient estimation ... See more keywords
Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models
Sign Up to like & getrecommendations! Published in 2022 at "Entropy"
DOI: 10.3390/e24020152
Abstract: Latent variable models (LVMs) for neural population spikes have revealed informative low-dimensional dynamics about the neural data and have become powerful tools for analyzing and interpreting neural activity. However, these approaches are unable to determine… read more here.
Keywords: dynamic graph; latent variable; variable models; graph ... See more keywords