Gaussian copula distributions for mixed data, with application in discrimination
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Publication:5222430
DOI10.1080/00949655.2015.1077386OpenAlexW2295773027MaRDI QIDQ5222430
Alexander R. de Leon, N. Withanage, Beilei Wu, F. Jiryaie
Publication date: 1 April 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2015.1077386
probit modelnormal distributionlogit model\(t\)-distributiongeneral location modelconditional grouped continuous model
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Cites Work
- Normal correlation coefficient of non-normal variables using piece-wise linear approximation
- The meta-elliptical distributions with given marginals
- On composite marginal likelihoods
- On multivariate Gaussian copulas
- Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients
- Efficient estimation in the bivariate normal copula model: Normal margins are least favourable
- A bivariate meta-Gaussian density for use in hydrology
- Gaussian copula marginal regression
- Estimation and inference for dependence in multivariate data
- Pairwise likelihood approach to grouped continuous model and its extension
- Correlated data analysis: modeling, analytics, and applications
- General mixed-data model: Extension of general location and grouped continuous models
- Joint Regression Analysis of Correlated Data Using Gaussian Copulas
- Discrimination and Classification Using Both Binary and Continuous Variables
- A Correlated Probit Model for Joint Modeling of Clustered Binary and Continuous Responses
- Multivariate Dispersion Models Generated From Gaussian Copula
- PARTIAL CORRELATION AND CONDITIONAL CORRELATION AS MEASURES OF CONDITIONAL INDEPENDENCE
- The Grouped Continuous Model for Multivariate Ordered Categorical Variables and Covariate Adjustment
- Bayesian Analysis of Binary and Polychotomous Response Data
- Applied Bayesian Modeling and Causal Inference from Incomplete‐Data Perspectives
- Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles
- Multivariate Correlation Models with Mixed Discrete and Continuous Variables
