Bayesian bridge-randomized penalized quantile regression for ordinal longitudinal data, with application to firm’s bond ratings

Tian, Yuzhu; Tang, Man‐Lai; Chan, Wai-Sum; Tian, Maozai

doi:10.1007/s00180-020-01037-4

Cited by 4 publications

(2 citation statements)

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“…introduced Bayesian quantile analysis of ordinal data and proposed two efficient MCMC algorithms. Since , ordinal quantile regression has attracted some attention, such as in Alhamzawi (2016), Alhamzawi and Ali (2018), Ghasemzadeh, Ganjali, and Baghfalaki (2018), Rahman and Karnawat (2019), Ghasemzadeh, Ganjali, and Baghfalaki (2020), and Tian et al (2021).…”

Section: Introductionmentioning

confidence: 99%

bqror: An R package for Bayesian Quantile Regression in Ordinal Models

Maheshwari,

Arshad Rahman

2023

The R Journal

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This article describes an R package bqror that estimates Bayesian quantile regression in ordinal models introduced in Rahman (2016). The paper classifies ordinal models into two types and offers computationally efficient yet simple Markov chain Monte Carlo (MCMC) algorithms for estimating ordinal quantile regression. The generic ordinal model with 3 or more outcomes (labeled OR I model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings algorithm, whereas an ordinal model with exactly 3 outcomes (labeled OR I I model) is estimated using a Gibbs sampling algorithm only. In line with the Bayesian literature, we suggest using the marginal likelihood for comparing alternative quantile regression models and explain how to compute it. The models and their estimation procedures are illustrated via multiple simulation studies and implemented in two applications. The article also describes several functions contained within the bqror package, and illustrates their usage for estimation, inference, and assessing model fit.

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Section: Introductionmentioning

confidence: 99%

bqror: An R package for Bayesian Quantile Regression in Ordinal Models

Maheshwari,

Arshad Rahman

2023

The R Journal

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“…Since Rahman (2016), ordinal quantile regression has attracted some attention. Some recent works with ordinal outcomes include Alhamzawi (2016), Alhamzawi and Ali (2018), Ghasemzadeh et al (2018), Rahman and Karnawat (2019), Ghasemzadeh et al (2020), and Tian et al (2021).…”

Section: Introductionmentioning

confidence: 99%

bqror: An R package for Bayesian Quantile Regression in Ordinal Models

Maheshwari¹,

Rahman²

2021

Preprint

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This article describes an R package bqror that estimates Bayesian quantile regression for ordinal models introduced in Rahman (2016). The paper classifies ordinal models into two types and offers two computationally efficient, yet simple, MCMC algorithms for estimating ordinal quantile regression. The generic ordinal model with more than 3 outcomes (labeled OR I model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR I I model) is estimated using Gibbs sampling only. In line with the Bayesian literature, we suggest using marginal likelihood for comparing alternative quantile regression models and explain how to calculate the same. The models and their estimation procedures are illustrated via multiple simulation studies and implemented in the two applications presented in Rahman ( 2016). The article also describes several other functions contained within the bqror package, which are necessary for estimation, inference, and assessing model fit.

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