Iterative distributed multinomial regression
Abstract
This article introduces an iterative distributed computing estimator for the multinomial logistic regression model with large choice sets. Compared to the maximum likelihood estimator, the proposed iterative distributed estimator achieves significantly faster computation and, when initialized with a consistent estimator, attains asymptotic efficiency under a weak dominance condition. Additionally, we propose a parametric bootstrap inference procedure based on the iterative distributed estimator and establish its consistency. Extensive simulation studies validate the effectiveness of the proposed methods and highlight the computational efficiency of the iterative distributed estimator.
Fields of Study & Subjects
Face and Expression Recognition
Advanced Statistical Methods and Models
Neural Networks and Applications
Publication details
- Journal / source
- Journal of Econometrics
- Publisher
- Elsevier BV
- Volume
- 258
- Pages
- 106334–106334
- Publication date
- 4 September 2026
- Language
- EN