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Found 10 papers, 6 papers with code
Scalable Kernel Logistic Regression with Nyström Approximation: Theoretical Analysis and Application to Discrete Choice Modelling
Among these strategies, the k-means Nystr\"om KLR approach emerges as a successful solution for applying KLR to large datasets, particularly when combined with the L-BFGS-B and Adam optimisation methods.
A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections
In this paper, we present and apply an approach for estimating ride-sourcing demand at a disaggregate level using discrete choice models and multiple data sources.
ciDATGAN: Conditional Inputs for Tabular GANs
Then, we demonstrate that ciDATGAN can be used to unbias datasets with the help of well-chosen conditional inputs.
DATGAN: Integrating expert knowledge into deep learning for synthetic tabular data
We show that the best versions of the DATGAN outperform state-of-the-art generative models on multiple case studies.
Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms
The integration of the new algorithms in Discrete Choice Models estimation software will significantly reduce the time required for model estimation and therefore enable researchers and practitioners to explore new approaches for the specification of choice models.
Stochastic Optimization
Optimization and Control
Robust discrete choice models with t-distributed kernel errors
In a case study on transport mode choice behaviour, MNR and Gen-MNR outperform MNP by substantial margins in terms of in-sample fit and out-of-sample predictive accuracy.
Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices.
Variational Bayesian Inference for Mixed Logit Models with Unobserved Inter- and Intra-Individual Heterogeneity
Besides, the simulation study demonstrates that a parallelised implementation of the MSL estimator with analytical gradients is a viable alternative to MCMC in terms of both estimation accuracy and computational efficiency, as the MSL estimator is observed to be between 0. 9 and 2. 1 times faster than MCMC.
Methodology Econometrics
Pólygamma Data Augmentation to address Non-conjugacy in the Bayesian Estimation of Mixed Multinomial Logit Models
The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involves sampling from conditional densities of utility parameters using Metropolis-Hastings (MH) algorithm due to unavailability of conjugate prior for logit kernel.
Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations
To address the latter, we conduct an extensive simulation-based evaluation to benchmark the extended VB methods against MCMC and MSLE in terms of estimation times, parameter recovery and predictive accuracy.
