Robust discrete choice models with t-distributed kernel errors

Krueger, Rico; Bansal, Prateek; Bierlaire, Michel; Gasos, Thomas

doi:10.1007/s11222-022-10182-3

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…Kim et al (2008) proposed more flexible generalised

t

‐link models for binary response data to solve model misspecification issue. More recent literature has been introduced to address the robustness issue for binary outcome analysis caused by the model misspecification problem (Krueger et al, 2023; Scalera et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Ordered probit Bayesian additive regression trees for ordinal data

Lee,

Hwang

2024

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Bayesian additive regression trees (BART) is a nonparametric model that is known for its flexibility and strong statistical foundation. To address a robust and flexible approach to analyse ordinal data, we extend BART into an ordered probit regression framework (OPBART). Further, we propose a semiparametric setting for OPBART (semi‐OPBART) to model covariates of interest parametrically and confounding variables nonparametrically. We also provide Gibbs sampling procedures to implement the proposed models. In both simulations and real data studies, the proposed models demonstrate superior performance over other competing ordinal models. We also highlight enhanced interpretability of semi‐OPBART in terms of inference through marginal effects.

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“…Kim et al (2008) proposed more flexible generalised