Quantile Regression Neural Networks: A Bayesian Approach

Author:

Jantre S. R.,Bhattacharya S.,Maiti T.ORCID

Publisher

Springer Science and Business Media LLC

Subject

Statistics and Probability

Reference43 articles.

1. Alhamzawi R (2018) “Brq”: R package for bayesian quantile regression. https://cran.r-project.org/web/packages/Brq/Brq.pdf

2. Andrews DF, Mallows CL (1974) Scale mixtures of normal distributions. J R Stat Soc Ser B (Methodol) 36:99–102

3. Barndorff-Nielsen OE, Shephard N (2001) Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics. J R Stat Soc Ser B 63:167–241

4. Barron A, Schervish MJ, Wasserman L (1999) The consistency of posterior distributions in nonparametric problems. Ann Stat 10:536–561

5. Benoit DF, Alhamzawi R, Yu K, den Poel DV (2017) R package ‘bayesQR’. https://cran.r-project.org/web/packages/bayesQR/bayesQR.pdf

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