The R package mvord implements composite likelihood estimation in the class of multivariate ordinal regression models with a multivariate probit and a multivariate logit link. A flexible modeling framework for multiple ordinal measurements on the same subject is set up, which takes into consideration the dependence among the multiple observations by employing different error structures. Heterogeneity in the error structure across the subjects can be accounted for by the package, which allows for covariate dependent error structures. In addition, different regression coefficients and threshold parameters for each response are supported. If a reduction of the parameter space is desired, constraints on the threshold as well as on the regression coefficients can be specified by the user. The proposed multivariate framework is illustrated by means of a credit risk application.

Correlated ordinal data typically arises from multiple measurements on a collection of subjects. Motivated by an application in credit risk, where multiple credit rating agencies assess the creditworthiness of a firm on an ordinal scale, we consider multivariate ordinal regression models with a latent variable specification and correlated error terms. Two different link functions are employed, by assuming a multivariate normal and a multivariate logistic distribution for the latent variables underlying the ordinal outcomes. Composite likelihood methods, more specifically the pairwise and tripletwise likelihood approach, are applied for estimating the model parameters. Using simulated data sets with varying number of subjects, we investigate the performance of the pairwise likelihood estimates and find them to be robust for both link functions and reasonable sample size. The empirical application consists of an analysis of corporate credit ratings from the big three credit rating agencies (Standard & Poor's, Moody's and Fitch). Firm-level and stock price data for publicly traded US firms as well as an unbalanced panel of issuer credit ratings are collected and analyzed to illustrate the proposed framework.

In a "publish-or-perish culture", the ranking of scientific journals plays a central role in assessing the performance in the current research environment. With a wide range of existing methods for deriving journal rankings, meta-rankings have gained popularity as a means of aggregating different information sources. In this paper, we propose a method to create a meta-ranking using heterogeneous journal rankings. Employing a parametric model for paired comparison data we estimate quality scores for 58 journals in the OR/MS/POM community, which together with a shrinkage procedure allows for the identification of clusters of journals with similar quality. The use of paired comparisons provides a flexible framework for deriving an aggregated score while eliminating the problem of missing data.

The number of undetected cases of SARS-CoV-2 infections is expected to be a multipleof the reported figures mainly due to the high ratio of asymptomatic infections and tolimited availability of trustworthy testing resources. Relying on the deCODE study inIceland, which offers large scale testing among the general population, we investigate themagnitude and uncertainty of the number of undetected cases COVID-19 cases in Austria.We formulate several scenarios relying on data on the number of COVID-19 cases whichhave been hospitalized, in intensive care, as well as on the number of deaths and positivetests in Iceland and Austria. We employ frequentist and Bayesian methods for estimatingthe dark figure in Austria based on the hypothesized scenarios and for accounting for theuncertainty surrounding this figure.Using data available on April 1, 2020, our study contains two main findings: First, wefind the estimated number of infections to be on average around 8.35 times higher thanthe recorded number of infections. Second, the width of the uncertainty bounds associatedwith this figure depends highly on the statistical method employed. At a 95% level, lowerbounds range from 3.96 to 6.83 and upper bounds range from 9.82 to 12.61. Overall, ourfindings confirm the need for systematic tests in the general population of Austria.

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