"Multivariate Ordinal Regression Models: An Anal- ysis of Corporate Credit Ratings." Research Report Series / Department of Statistics and Mathematics 132, WU Vienna University of Economics and Business, Vienna. URL http://epub.wu.ac.at/5389/.Hirk, R., Hornik, K., Vana, L., 2018. ...
Hirk, R., Hornik, K., Vana, L., 2018. Multivariate ordinal regression models: an analysis of corporate credit ratings. Statistical Methods & Applications doi:doi.org/10.1007/s10260-018-00437-7.Hirk R, Hornik K, Vana L (2017). "Multivariate Ordinal Regression Models: An Anal- ysis of ...
For this reason, logistic models may be preferred. In generalizing the logistic regression model to account for multivariate categorical out- comes, normally distributed random effects are commonly incorporated (Diggle, Liang and Zeger, 1994; Stiratelli et al., 1984). Posterior computation can ...
This model was the most parsimonious of the models evaluated and demonstrated good fit (Hosmer-Lemeshow test p = 0.471) and discrimination (area under ROC curve = 0.906). There were no significant interaction terms. Conclusions: The authors used multivariate logistic regression to identify a set ...
Data and Models 9.5 More Data Analysis Methods 9.5.1 Multivariate Analysis Multivariate analysis is a set of techniques used for analysis of data sets that contain more than one variable, and the techniques are especially valuable when working with correlated variables. The techniques provide an empi...
A parameter of network models that controls the entropy of node state patterns. A network with low temperature will allow only node states that align, such that positively connected nodes must be in the same state and negatively connected nodes must be in the opposite state, whereas a network ...
General or generalized linear models (GLM) Regression: Heckman selection model to reduce bias Multivariate Methods: Hotelling's T2 Discriminant analysis Mahalanobis’ distance Multivariate analysis of variance (MANOVA) and covariance (MANCOVA) Principal components analysis Factor analysis Cluster analysis Nonp...
MultiPhen. This test9 performs a 'reversed regression', with multiple phenotype predictors and genetic variant as outcome. Since genotypes of SNPs (and other genetic variants) correspond to ordinal data, an ordinal regression is performed here. This test has been shown to be equivalent to ...
Feature selection is a statistical process that can be applied to both regression and classification models. In this study, we focused on distinguishing AF from NSR using Holter-based ECG data and applied feature selection in the context of supervised classification models. Feature selection methods ...
In multivariate statistical analysis, control variables are often included in the regression models that are run. Here model refers to a particular set of variables that are included in an analysis, along with the dependent and independent variables. It is not unusual for an investigator to run ...