Results 31 to 40 of about 121,305 (186)

A Bayesian semiparametric latent variable model for mixed responses [PDF]

, 2006
In this article we introduce a latent variable model (LVM) for mixed ordinal and continuous responses, where covariate effects on the continuous latent variables are modelled through a flexible semiparametric predictor. We extend existing LVM with simple
Fahrmeir, Ludwig, Raach, Alexander
core   +4 more sources

The Construction of Patient Loyalty Model Using Bayesian Structural Equation Modeling Approach

Cauchy: Jurnal Matematika Murni dan Aplikasi, 2018
The information on the health status of an individual is often gathered based on a health survey. Patient assessment on the quality of hospital services is important as a reference in improving the service so that it can increase a patient satisfaction ...
Astari Rahmadita, Ferra Yanuar, Dodi Devianto   +2 more
doaj   +1 more source

Gaussian Process Panel Modeling—Machine Learning Inspired Analysis of Longitudinal Panel Data

Frontiers in Psychology, 2020
In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. We call this new approach Gaussian Process Panel Modeling (GPPM).
Julian D. Karch, Julian D. Karch, Andreas M. Brandmaier, Andreas M. Brandmaier, Manuel C. Voelkle   +4 more
doaj   +1 more source

Ensemble evaluation of hydrological model hypotheses [PDF]

, 2010
It is demonstrated for the first time how model parameter, structural and data uncertainties can be accounted for explicitly and simultaneously within the Generalized Likelihood Uncertainty Estimation (GLUE) methodology.
Abrahart, Ajami, Ajami, Armstrong, Beck, Beven, Beven, Beven, Boyle, Butts, Chow, Christopher J. A. Macleod, Clark, Duan, Freer, Freer, Freer, Gary S. Bilotta, Georgakakos, Harmel, Hsu, Huard, Huard, Jim Freer, John N. Quinton, Jothityangkoon, Kavetski, Kavetski, Kavetski, Kavetski, Kennedy, Kirkby, Klemeš, Kuczera, Liu, Marshall, Nash, Neuman, Pappenberger, Pappenberger, Patricia Butler, Philip M. Haygarth, Poeter, Priestley, Richard E. Brazier, Richards, See, See, Shamseldin, Shamseldin, Sivapalan, Smith, Thornley, Tobias Krueger, Vache, Vereecken, Vrugt, Vrugt, Vrugt, Vrugt, Vrugt, Wagener, Wagener, Wagener, Xiong, Ye, Young, Younger   +67 more
core   +2 more sources

Improving Service Quality of Metro Systems—A Case Study in the Beijing Metro

IEEE Access, 2020
In this study, we propose a method that combines Bayesian network, structural equation modeling, and importance-performance analyses to evaluate and improve the service quality of crowded metros from the point of service components.
Xinyue Xu, Yi Lu, Yingxiang Wang, Jianmin Li, Hanyu Zhang   +4 more
doaj   +1 more source

Using the Effective Sample Size as the Stopping Criterion in Markov Chain Monte Carlo with the Bayes Module in Mplus

Psych, 2021
Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation requires researchers to decide not only whether estimation has converged but also whether the Bayesian estimates are well-approximated by summary statistics from the chain.
Steffen Zitzmann, Sebastian Weirich, Martin Hecht   +2 more
doaj   +1 more source

HYPER-PARAMETER SELECTION IN BAYESIAN STRUCTURAL EQUATION MODELS [PDF]

Bulletin of informatics and cybernetics, 2010
Summary: In the structural equation models, the maximum likelihood estimates of error variances can often turn out to be zero or negative. In order to overcome this problem, we take a Bayesian approach by specifying a prior distribution for variances of error variables. Crucial issues in this modeling procedure include the selection of hyper-parameters
Hirose, Kei, Kawano, Shuichi, Miike, Daisuke, Konishi, Sadanori   +3 more
openaire   +2 more sources

A Bayesian Modeling Approach for Generalized Semiparametric Structural Equation Models [PDF]

Psychometrika, 2013
In behavioral, biomedical, and psychological studies, structural equation models (SEMs) have been widely used for assessing relationships between latent variables. Regression-type structural models based on parametric functions are often used for such purposes.
Song, Xin-Yuan, Lu, Zhao-Hua, Cai, Jing-Heng, Ip, Edward Hak-Sing   +3 more
openaire   +3 more sources

Prior Specification for More Stable Bayesian Estimation of Multilevel Latent Variable Models in Small Samples: A Comparative Investigation of Two Different Approaches

Frontiers in Psychology, 2021
Bayesian approaches for estimating multilevel latent variable models can be beneficial in small samples. Prior distributions can be used to overcome small sample problems, for example, when priors that increase the accuracy of estimation are chosen. This
Steffen Zitzmann, Christoph Helm, Martin Hecht   +2 more
doaj   +1 more source

Contributions to Bayesian Structural Equation Modeling [PDF]

, 2010
Structural equation models (SEMs) are multivariate latent variable models used to model causality structures in data. A Bayesian estimation and validation of SEMs is proposed and identifiability of parameters is studied. The latter study shows that latent variables should be standardized in the analysis to ensure identifiability.
Demeyer, Séverine, Fischer, Nicolas, Saporta, Gilbert   +2 more
openaire   +2 more sources