J. F. Vera
Logistic regression models are a powerful research tool for the analysis of cross-classified data in which a categorical response variable is involved. In a logistic model, the effect of a covariate refers to odds, and the simple relationship between the coefficients and the odds ratio often makes these the parameters of interest due to its easy interpretation. In this work we present a distance-based logistic model that allows a simple graphical interpretation of the association coefficients using the odds ratio in a contingency table. Two configurations are estimated, one for the rows and one for the columns, as the categories of a polytomous predictor and a nominal response variable respectively, such that the local odds ratio and the distances between the predictor and response categories are inversely related. The performance of the estimation procedure is analyzed for both real and simulated data sets.
Keywords: Contingency table, categorical predictor, distances, multinomial baseline-category logit model, odds ratio, unfolding.
Scheduled
GT04 Multivariate Analysis and Classification III
June 7, 2022 4:50 PM
Cloister room
