In this paper we have combined fundamental analysis and contingent claim analysis into a hybrid model of credit risk measurement. We have extended the standard Merton approach to estimate a new risk neutral distance to default metric, assuming a more complex capital structure, adjusting for dividend payments, introducing randomness to the default point and allowing a fractional recovery when de...

Classical regression analysis relates the expectation of a response variable to a linear combination of explanatory variables. In this article, we propose a covariance regression model that parameterizes the covariance matrix of a multivariate response vector as a parsimonious quadratic function of explanatory variables. The approach can be seen as analogous to the mean regression model, and ha...

Online forums (synchronous and asynchronous) offer exciting data opportunities to analyze how people influence one another through their interactions. However, researchers must address several analytic difficulties involving the data (missing values, nested structure [messages within topics], non‐sequential messages), outcome variables (discrete outcomes, rare instances, m...

Bayes-N is an algorithm for Bayesian network learning from data based on local measures of information gain, applied to problems in which there is a given dependent or class variable and a set of independent or explanatory variables from which we want to predict the class variable on new cases. Given this setting, Bayes-N induces an ancestral ordering of all the variables generating a directed ...

A new stepwise regression program (SROV) for the construction of optimal (stable and of highest possible accuracy) regression models comprised of linear combination of independent variables and their non-linear functions is described. The program uses for regression QR decomposition based on Gram /Schmidth orthogonalization, which is highly resilient to numerical error propagation. Variables ar...

Naturalization is an important phenomenon for countries, not only because of its implications (it grants duties and rights to new citizens) but also because of its policy-sensitive aspect. In Switzerland, it is also a complex phenomenon because of the diversity of procedures at the canton and commune levels. Knowledge of the determinants of naturalization is still lacking. In this context, this...

Poor measurement of explanatory variables occurs frequently in observational studies. Error-prone observations may lead to biased estimation and loss of power in detecting the impact of explanatory variables on the response. We consider misclassified binary exposure in the context of case-control studies, assuming the availability of validation data to inform the magnitude of the misclassificat...

To enable answering the question in the title, we introduce a bivariate censored latent eeects autoregression, and discuss representation, parameter estimation, di-agnostics and inference. We show that this bivariate nonlinear model is very useful for examining common nonlinearity. We apply the model to the monthly unemployment rate in the US and Canada to examine if these variables have common...

In the analysis of multivariate dependence, two main objectives may be pursued: descriptive and decisional. In the descriptive approach, the selection of explanatory variables is determined by the researcher’s objectives and form the nature of the problem at hand. The decisional approach requires a parsimonious model where only the variables with the highest explanatory power are included. This...

In analytical chemistry, multivariate calibration is applied when substituting a time-consuming reference measurement (based on e.g. chromatography) with high-throughput vibrational spectroscopy). An average error term, of the response variable, often used to evaluate performance model. However, indirect relationships, between and explanatory variables, may be for calibration. such cases, model...