Fast Estimation of Multinomial Logit Models: R Package mnlogit
Computation
2014-09-17 v2
Abstract
We present R package mnlogit for training multinomial logistic regression models, particularly those involving a large number of classes and features. Compared to existing software, mnlogit offers speedups of 10x-50x for modestly sized problems and more than 100x for larger problems. Running mnlogit in parallel mode on a multicore machine gives an additional 2x-4x speedup on up to 8 processor cores. Computational efficiency is achieved by drastically speeding up calculation of the log-likelihood function's Hessian matrix by exploiting structure in matrices that arise in intermediate calculations.
Keywords
Cite
@article{arxiv.1404.3177,
title = {Fast Estimation of Multinomial Logit Models: R Package mnlogit},
author = {Asad Hasan and Wang Zhiyu and Alireza S. Mahani},
journal= {arXiv preprint arXiv:1404.3177},
year = {2014}
}