Bayesian Quantile Regression for Ordinal Models
cdf of an asymmetric Laplace distribution
cdf of a standard asymmetric Laplace distribution
Bayesian quantile regression for ordinal models
Covariate effect in the OR1 model
Covariate effect in the OR2 model
Extractor function for summary
Extractor function for summary
Deviance Information Criterion in the OR1 model
Deviance Information Criterion in the OR2 model
Samples in the OR1 model
Samples in the OR2 model
Samples in the OR1 model
Samples latent variable z in the OR1 model
Samples latent variable z in the OR2 model
Samples scale factor in the OR2 model
Samples in the OR2 model
Samples latent weight w in the OR1 model
Inefficiency factor in the OR1 model
Inefficiency factor in the OR2 model
Marginal likelihood in the OR1 model
Marginal likelihood in the OR2 model
Minimizes the negative of log-likelihood in the OR1 model
Negative log-likelihood in the OR1 model
Negative sum of log-likelihood in the OR2 model
Bayesian quantile regression in the OR1 model
Bayesian quantile regression in the OR2 model
Generates random numbers from an AL distribution
Package provides functions for estimation and inference in Bayesian quantile regression with ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm. The summary output presents the posterior mean, posterior standard deviation, 95% credible intervals, and the inefficiency factors along with the two model comparison measures – logarithm of marginal likelihood and the deviance information criterion (DIC). The package also provides functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models. Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, 11(1): 1-24 <doi: 10.1214/15-BA939>. Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and Probability Letters, 54(4): 437–447 <doi: 10.1016/S0167-7152(01)00124-9>. Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica, 46(1): 33-50 <doi: 10.2307/1913643>. Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90(432):1313–1321, 1995. <doi: 10.1080/01621459.1995.10476635>. Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. <doi: 10.1198/016214501750332848>.