General mixed-data model: Extension of general location and grouped continuous models

DOI10.1002/cjs.5550350405zbMath1143.62323OpenAlexW2059088896MaRDI QIDQ3512632

Alexander R. de Leon, Keumhee Chough Carrière

Publication date: 21 July 2008

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.5550350405

zbMATH Keywords

probit modelmaximum likelihoodmultivariate normal distributionlatent variable modelspolyserial correlationpolychoric correlationmeasurement levelforeign language achievement study

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Measures of association (correlation, canonical correlation, etc.) (62H20)

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Cites Work

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• Longitudinal data analysis using generalized linear models
• The polyserial correlation coefficient
• A pairwise likelihood approach to estimation in multilevel probit models
• Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients
• Maximum likelihood estimation of the polychoric correlation coefficient
• Conditionally specified distributions
• The location model for mixtures of categorical and continuous variables
• A pairwise likelihood approach to analyzing correlated binary data
• A generalized Mahalanobis distance for mixed data
• Models for discrete longitudinal data.
• Pairwise likelihood approach to grouped continuous model and its extension
• Estimation of polychoric correlation with elliptical latent variables
• A note on pseudolikelihood constructed from marginal densities
• Ranges of Measures of Association for Familial Binary Variables
• Two-step estimation of multivariate polychoric correlation
• A Composite Likelihood Approach to Binary Spatial Data
• Estimating the Mahalanobis Distance from Mixed Continuous and Discrete Data
• The Grouped Continuous Model for Multivariate Ordered Categorical Variables and Covariate Adjustment
• Latent Variable Models for Clustered Ordinal Data
• Regression Models for a Bivariate Discrete and Continuous Outcome with Clustering
• Latent Variable Models with Mixed Continuous and Polytomous Data
• Estimation of the parameters for a multivariate normal distribution when one variable is dichotomized
• Multivariate Correlation Models with Mixed Discrete and Continuous Variables

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