Multivariate zero-inflated modeling with latent predictors: modeling feedback behavior

DOI10.1016/j.csda.2013.07.003zbMath1471.62062OpenAlexW2086868218MaRDI QIDQ1615161

Jean-Paul Fox

Publication date: 2 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.07.003

zbMATH Keywords

Poisson-gamma modelfeedback behavioritem response timeslatent predictorsmultivariate zero-inflated count dataPoisson-log normal model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Applications of statistics to psychology (62P15)

Related Items (5)

Clustering for time-varying relational count data ⋮ The LZIP: A Bayesian latent factor model for correlated zero‐inflated counts ⋮ Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model ⋮ Type I multivariate zero-truncated/adjusted Poisson distributions with applications ⋮ First-order mixed integer-valued autoregressive processes with zero-inflated generalized power series innovations

Uses Software

• R
• cirt
• BayesDA

Cites Work

• Elements of adaptive testing
• Bayesian item response modeling. Theory and applications.
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• A Bayesian analysis of the multinomial probit model with fully identified parameters
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• Multilevel and latent variable modeling with composite links and exploded likelihoods
• A hierarchical framework for modeling speed and accuracy on test items
• The analysis of zero-inflated count data: Beyond zero-inflated Poisson regression.
• Multiple-Imputation-Based Residuals and Diagnostic Plots for Joint Models of Longitudinal and Survival Outcomes
• Joint modelling of mixed outcome types using latent variables
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• The multivariate Poisson-log normal distribution
• A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use
• Bayesian Models for Categorical Data
• Random effect models for repeated measures of zero-inflated count data
• Post-Processing Posterior PredictivepValues
• Unnamed Item
• Unnamed Item

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