Fitting mixed logit random regret minimization models using maximum simulated likelihood
This article describes the mixrandregret command, which extends the randregret command introduced in Guti\'errez-Vargas et al. (2021, The Stata Journal 21: 626-658) incorporating random coefficients for Random Regret Minimization models. The newly developed command mixrandregret allows the inclusion of random coefficients in the regret function of the classical RRM model introduced in Chorus (2010, European Journal of Transport and Infrastructure Research 10: 181-196). The command allows the user to specify a combination of fixed and random coefficients. In addition, the user can specify normal and log-normal distributions for the random coefficients using the commands' options. The models are fitted using simulated maximum likelihood using numerical integration to approximate the choice probabilities.
Code (0)
등록된 구현이 없습니다.
Tasks
Numerical IntegrationSimilar Papers 제목 키워드 기반
The Mixed Aggregate Preference Logit Model: A Machine Learning Approach to Modeling Unobserved Heterogeneity in Discrete Choice Analysis
This paper introduces the Mixed Aggregate Preference Logit (MAPL, pronounced "maple'') model, a novel class of discrete choice models that leverages machine learning to model unobserved heterogeneity in discrete choice a…
Decision MakingDiagnosticDiscrete Choice ModelsOptimizing B2B Product Offers with Machine Learning, Mixed Logit, and Nonlinear Programming
In B2B markets, value-based pricing and selling has become an important alternative to discounting. This study outlines a modeling method that uses customer data (product offers made to each current or potential customer…
Evolutionary Dynamics and $Φ$-Regret Minimization in Games
Regret has been established as a foundational concept in online learning, and likewise has important applications in the analysis of learning dynamics in games. Regret quantifies the difference between a learner's perfor…
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration
This paper studies regret minimization with randomized value functions in reinforcement learning. In tabular finite-horizon Markov Decision Processes, we introduce a clipping variant of one classical Thompson Sampling (T…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Thompson SamplingOptimal Regret Bounds for Collaborative Learning in Bandits
We consider regret minimization in a general collaborative multi-agent multi-armed bandit model, in which each agent faces a finite set of arms and may communicate with other agents through a central controller. The opti…