Ordinal logistic regression is a powerful tool for modeling the dependence relationship between a set of regressors and an ordinal response variable. However, it necessitates careful consideration of multicollinearity among the regressors and examination for outliers. For dealing with these problems in ordinal logistic regression models, we develop an approach based on robust principal components. The new robust approach will be applied for assessing judges’ performances.
Dealing with multicollinearity and outliers in ordinal logit model
I. Camminatiello
;
2019
Abstract
Ordinal logistic regression is a powerful tool for modeling the dependence relationship between a set of regressors and an ordinal response variable. However, it necessitates careful consideration of multicollinearity among the regressors and examination for outliers. For dealing with these problems in ordinal logistic regression models, we develop an approach based on robust principal components. The new robust approach will be applied for assessing judges’ performances.File in questo prodotto:
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