Multilevel and latent variable modeling with composite links and exploded likelihoods
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Publication:2517865
DOI10.1007/s11336-006-1453-8zbMath1286.62108OpenAlexW2141075033WikidataQ60655829 ScholiaQ60655829MaRDI QIDQ2517865
Anders Skrondal, Sophia Rabe-Hesketh
Publication date: 12 January 2009
Published in: Psychometrika (Search for Journal in Brave)
Full work available at URL: http://www.escholarship.org/uc/item/2cm0s5dr
frailtyunfoldingfactor modelmultilevel modelGLLAMMgeneralized linear mixed modellatent variable modelitem response modelzero-inflated Poisson modelcomposite linkexploded likelihood
Related Items (7)
Multivariate zero-inflated modeling with latent predictors: modeling feedback behavior ⋮ Finite mixture multilevel multidimensional ordinal IRT models for large scale cross-cultural research ⋮ Classical latent variable models for medical research ⋮ Latent Variable Modelling: A Survey* ⋮ The randomized response log linear model as a composite link model ⋮ Ill-posed problems with counts, the composite link model and penalized likelihood ⋮ Efficient likelihood estimation of generalized structural equation models with a mix of normal and nonnormal responses
Uses Software
Cites Work
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- A General Approach to Analyzing Epidemiologic Data that Contain Misclassification Errors
- Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects
- On the relationship between item response theory and factor analysis of discretized variables
- A taxonomy of item response models
- A general approach to categorical data analysis with missing data, using generalized linear models with composite links
- Statistical analysis of threshold data from experiments with nested errors
- Maximum likelihood estimation of latent interaction effects with the LMS method
- Multilevel logistic regression for polytomous data and rankings
- Generalized multilevel structural equation modeling
- Nonparametric estimation of item and respondent locations from unfolding-type items
- Mixed-effects analyses of rank-ordered data
- Bayesian estimation of a multilevel IRT model using Gibbs sampling
- Item randomized-response models for measuring noncompliance: risk-return perceptions, social influences, and self-protective responses
- A general model for the analysis of multilevel data
- Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
- A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data
- The Case for Small Area Microdata
- Maximum Likelihood Analysis of a General Latent Variable Model with Hierarchically Mixed Data
- Latent class and finite mixture models for multilevel data sets
- Composite Link Functions in Generalized Linear Models
- Fitting Cox's Regression Model to Survival Data using GLIM
- Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
- Life Tables with Concomitant Information
- Nonparametric Maximum Likelihood Estimation of a Mixing Distribution
- Analysis of Longitudinal Binary Data from Multiphase Sampling
- Empirical Bayesian Estimators for a Poisson Process Propagated in Time
- Random Effects Models in Latent Class Analysis for Evaluating Accuracy of Diagnostic Tests
- Estimation in Forest Yield Models Using Composite Link Functions with Random Effects
- A hyperbolic cosine latent trait model for unfolding polytomous responses: Reconciling Thurstone and Likert methodologies
- Zero‐Inflated Poisson and Binomial Regression with Random Effects: A Case Study
- Approximate Inference in Generalized Linear Mixed Models
- Beyond SEM: General Latent Variable Modeling
- On the Analysis of Grouped Extreme Value Data with GLIM of Fixed Source
- Bias and efficiency loss due to misclassified responses in binary regression
- Multilevel models for censored and latent responses
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