The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis. Issue 12 (9th September 2020)

• Record Type: Journal Article
• Authors: Hashimoto, E. M.; Ortega, E. M. M.; Cordeiro, G. M.; Suzuki, A. K.; Kattan, M. W.
• Is Part Of: Journal of applied statistics. Volume 47:Issue 12(2020)
• Page Start: 2159
• Page End: 2177
• Publication Date: 2020-09-09
• Subjects: Categorical data -- diagnostic analysis -- multinomial distribution -- nominal response -- regression model; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2019.1706725
• Languages: English
• ISSNs: 0266-4763

Abstract

Abstract : The multinomial logistic regression model (MLRM) can be interpreted as a natural extension of the binomial model with logit link function to situations where the response variable can have three or more possible outcomes. In addition, when the categories of the response variable are nominal, the MLRM can be expressed in terms of two or more logistic models and analyzed in both frequentist and Bayesian approaches. However, few discussions about post modeling in categorical data models are found in the literature, and they mainly use Bayesian inference. The objective of this work is to present classic and Bayesian diagnostic measures for categorical data models. These measures are applied to a dataset (status) of patients undergoing kidney transplantation.