Results 21 to 30 of about 121,305 (186)

Bayesian Lasso for Semiparametric Structural Equation Models [PDF]

Biometrics, 2012
SummaryThere has been great interest in developing nonlinear structural equation models and associated statistical inference procedures, including estimation and model selection methods. In this paper a general semiparametric structural equation model (SSEM) is developed in which the structural equation is composed of nonparametric functions of ...
Guo, Ruixin, Zhu, Hongtu, Chow, Sy-Miin, Ibrahim, Joseph G.   +3 more
openaire   +3 more sources

Bayesian Regularized SEM: Current Capabilities and Constraints

Psych, 2023
An important challenge in statistical modeling is to balance how well our model explains the phenomenon under investigation with the parsimony of this explanation.
Sara van Erp
doaj   +1 more source

Planning and performance in teams: A Bayesian meta-analytic structural equation modeling approach.

PLoS ONE, 2023
We meta-analyzed the relationship between team planning and performance moderated by task, team, context, and methodological factors. For testing our hypothesized model, we used a meta-analytic structural equation modeling approach.
Udo Konradt, Alexander Nath, Martina Oldeweme   +2 more
doaj   +1 more source

blavaan: Bayesian Structural Equation Models via Parameter Expansion [PDF]

Journal of Statistical Software, 2018
This article describes blavaan, an R package for estimating Bayesian structural equation models (SEMs) via JAGS and for summarizing the results. It also describes a novel parameter expansion approach for estimating specific types of models with residual covariances, which facilitates estimation of these models in JAGS.
Merkle, Edgar C, Rosseel, Yves
openaire   +5 more sources

Structural Equation Modeling of Vocabulary Size and Depth Using Conventional and Bayesian Methods

Frontiers in Psychology, 2020
In classifications of vocabulary knowledge, vocabulary size and depth have often been separately conceptualized (Schmitt, 2014). Although size and depth are known to be substantially correlated, it is not clear whether they are a single construct or two ...
Rie Koizumi, Yo In’nami
doaj   +1 more source

A theory-based and data-driven approach to promoting physical activity through message-based interventions

Frontiers in Psychology, 2023
ObjectiveWe investigated how physical activity can be effectively promoted with a message-based intervention, by combining the explanatory power of theory-based structural equation modeling with the predictive power of data-driven artificial intelligence.
Patrizia Catellani, Marco Biella, Valentina Carfora, Antonio Nardone, Luca Brischigiaro, Marina Rita Manera, Marco Piastra   +6 more
doaj   +1 more source

Parameter Estimation of Structural Equation Modeling Using Bayesian Approach

Cauchy: Jurnal Matematika Murni dan Aplikasi, 2016
Leadership is a process of influencing, directing or giving an example of employees in order to achieve the objectives of the organization and is a key element in the effectiveness of the organization.
Dewi Kurnia Sari, Ni Wayan Surya Wardhani, Suci Astutik   +2 more
doaj   +1 more source

Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study

Pathogens, 2022
Pain can be present in up to 50% of people with post-COVID-19 condition. Understanding the complexity of post-COVID pain can help with better phenotyping of this post-COVID symptom.
César Fernández-de-las-Peñas, Bernard X. W. Liew, Manuel Herrero-Montes, Pablo del-Valle-Loarte, Rafael Rodríguez-Rosado, Diego Ferrer-Pargada, Randy Neblett, Paula Paras-Bravo   +7 more
doaj   +1 more source

Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors [PDF]

, 2009
Meaningful quantification of data and structural uncertainties in conceptual rainfall-runoff modeling is a major scientific and engineering challenge. This paper focuses on the total predictive uncertainty and its decomposition into input and structural ...
Atger, Benjamin Renard, Beven, Beven, Beven, Bras, Bulygina, Di Baldassarre, Dmitri Kavetski, Dottori, Duan, Eberly, Fenicia, Fenicia, Feyen, Franks, Gelfand, Gelman, George Kuczera, Goldstein, Haario, Hadamard, Hall, Huard, Jacquin, Kavetski, Kavetski, Kavetski, Kavetski, Kennedy, Krzysztofowicz, Kuczera, Kuczera, Kuczera, Laio, Mantovan, Mark Thyer, Marshall, Montanari, Montanari, Moradkhani, Moyeed, Neppel, Oudin, Perrin, Refsgaard, Reichert, Reitan, Renard, Severino, Spiegelhalter, Stedinger, Stewart W. Franks, Tarantola, Thiemann, Thyer, Tonkin, Wagener, Yang, Young   +59 more
core   +3 more sources

A review of R-packages for random-intercept probit regression in small clusters [PDF]

, 2016
Generalized Linear Mixed Models (GLMMs) are widely used to model clustered categorical outcomes. To tackle the intractable integration over the random effects distributions, several approximation approaches have been developed for likelihood-based ...
Josephy, Haeike, Loeys, Tom, Rosseel, Yves   +2 more
core   +3 more sources