Fast Estimation of Multinomial Logit Models: R Package mnlogit

@article{Hasan2014FastEO,
  title={Fast Estimation of Multinomial Logit Models: R Package mnlogit},
  author={A. Hasan and Wang Zhiyu and Alireza S. Mahani},
  journal={arXiv: Computation},
  year={2014}
}
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… Expand
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References

SHOWING 1-10 OF 52 REFERENCES
Regularization Paths for Generalized Linear Models via Coordinate Descent.
TLDR
In comparative timings, the new algorithms are considerably faster than competing methods and can handle large problems and can also deal efficiently with sparse features. Expand
Multinomial logistic regression algorithm
The lower bound principle (introduced in Böhning and Lindsay 1988, Ann. Inst. Statist. Math., 40, 641–663), Böhning (1989, Biometrika, 76, 375–383) consists of replacing the second derivative matrixExpand
The VGAM Package for Categorical Data Analysis
Classical categorical regression models such as the multinomial logit and proportional odds models are shown to be readily handled by the vector generalized linear and additive model (VGLM/VGAM)Expand
Extended Model Formulas in R : Multiple Parts and Multiple Responses
Model formulas are the standard approach for specifying the variables in statistical models in the S language. Although being eminently useful in an extremely wide class of applications, they haveExpand
Trust Region Newton Method for Logistic Regression
TLDR
This paper applies a trust region Newton method to maximize the log-likelihood of the logistic regression model, and extends the proposed method to large-scale L2-loss linear support vector machines (SVM). Expand
maxent: An R Package for Low-memory Multinomial Logistic Regression with Support for Semi-automated Text Classification
TLDR
The focus of this maximum entropy classifier is to minimize memory consumption on very large datasets, particularly sparse document-term matrices represented by the tm text mining package. Expand
Discrete Choice Methods with Simulation
TLDR
Discrete Choice Methods with Simulation by Kenneth Train has been available in the second edition since 2009 and contains two additional chapters, one on endogenous regressors and one on the expectation–maximization (EM) algorithm. Expand
A Numerical Study of the Limited Memory BFGS Method and the Truncated-Newton Method for Large Scale Optimization
This paper examines the numerical performances of two methods for large-scale optimization: a limited memory quasi-Newton method (L-BFGS), and a discrete truncated-Newton method (TN). Various ways ofExpand
Diagnostic Checking in Regression Relationships
TLDR
A rich variety of diagnostic tests for these situations have been developed in the econometrics community, a collection of which has been implemented in the packages lmtest and strucchange covering the problems mentioned above. Expand
On the limited memory BFGS method for large scale optimization
TLDR
The numerical tests indicate that the L-BFGS method is faster than the method of Buckley and LeNir, and is better able to use additional storage to accelerate convergence, and the convergence properties are studied to prove global convergence on uniformly convex problems. Expand
...
1
2
3
4
5
...