Multivariate ordinal regression models: an analysis of corporate credit ratings

DOI10.1007/s10260-018-00437-7zbMath1435.62424OpenAlexW2582984882WikidataQ129317513 ScholiaQ129317513MaRDI QIDQ2305032

Rainer Hirk, Laura Vana, Kurt Hornik

Publication date: 10 March 2020

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10260-018-00437-7

zbMATH Keywords

composite likelihoodlatent variable modelscredit ratingsfinancial ratiosmultivariate ordered probitmultivariate ordered logit

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Estimation in multivariate analysis (62H12)

Related Items (5)

Weighted scores estimating equations and CL1 information criteria for longitudinal ordinal response ⋮ Sparse concordance‐based ordinal classification ⋮ Composite Likelihood Estimation of an Autoregressive Panel Ordered Probit Model with Random Effects ⋮ Multivariate ordinal regression models: an analysis of corporate credit ratings ⋮ Effective transfer entropy to measure information flows in credit markets

Uses Software

• NEWUOA
• R
• rms
• mnormt
• numDeriv
• optimx
• mvord

Cites Work

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• Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis
• On composite marginal likelihoods
• Multivariate logistic distributions
• A Bayesian multivariate probit for ordinal data with semiparametric random-effects
• Multivariate ordinal regression models: an analysis of corporate credit ratings
• An approximate marginal logistic distribution for the analysis of longitudinal ordinal data
• Bivariate Logistic Distributions
• A comparison of the maximum simulated likelihood and composite marginal likelihood estimation approaches in the context of the multivariate ordered-response model
• Bayesian Multivariate Logistic Regression
• Modeling Ordered Choices
• EM algorithms for ordered probit models with endogenous regressors
• A note on composite likelihood inference and model selection
• Analysis of multivariate probit models
• Pairwise likelihood inference for multivariate ordinal responses with applications to customer satisfaction
• Modeling for Dynamic Ordinal Regression Relationships: An Application to Estimating Maturity of Rockfish in California
• Bayesian Analysis of Binary and Polychotomous Response Data

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