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Bayesian quantile regression for ordinal longitudinal data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Alhamzawi, Rahim Ali, Haithem Taha Mohammad |
| Copyright Year | 2016 |
| Abstract | ABSTRACT Since the pioneering work by Koenker and Bassett [27], quantile regression models and its applications have become increasingly popular and important for research in many areas. In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest. An efficient Gibbs sampling algorithm was derived for fitting the model to the data based on a location-scale mixture representation of the skewed double-exponential distribution. The proposed approach is illustrated using simulated data and a real data example. This is the first work to discuss quantile regression for analysis of longitudinal data with ordinal outcome. |
| Starting Page | 815 |
| Ending Page | 828 |
| Page Count | 14 |
| File Format | PDF HTM / HTML |
| DOI | 10.1080/02664763.2017.1315059 |
| Volume Number | 45 |
| Alternate Webpage(s) | https://arxiv.org/pdf/1603.00297v1.pdf |
| Alternate Webpage(s) | https://doi.org/10.1080/02664763.2017.1315059 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
