On multivariate quantile regression
By Biman Chakraborty. 2003
90 citations recorded by the source corpus.
Subfields: Applied Mathematics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/7a30c0a3997c09b1f95f66b8270bcb14219e2bd7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Applied Mathematics
- Thomas Kneib
- N. Klein
- Stefan Lang
- Ying Wei
- Simon N. Wood
- Andreas Brezger
- Ludwig Fahrmeir
- Simon N. Wood
In Statistics and Probability
- N. Klein
- Thomas Kneib
- Stefan Lang
- Stephan Klasen
- Ying Wei
- Ludwig Fahrmeir
- Andreas Brezger
- Simon N. Wood
Citations in the corpus
- Choosing Among Notions of Multivariate Depth Statistics (2020, unknown): “1007/s00780-007-0043-7 [9] CHAKRABORTY, B.”
- An Approach to Multivariate Covariate-Dependent Quantile Contours With Application to Bivariate Conditional Growth Charts (2008, methodology): “Other approaches have been given by Parzen (1979), Abdous and Theodorescu (1992), Hettmansperger, Nyblom, and Oja (1992), Chaudhuri (1996), Koltchinskii (1997), Chakraborty (2003), and McDermott and Lin (2007), to name just a few.”
- Bayesian structured additive distributional regression for multivariate responses (2015, background): “In particular, the distributional variant of seemingly unrelated regression models with all parameters depending on covariates provides a natural counterpart to recent attempts to define bivariate quantile regression models; see for example Chakraborty (2003) who considered linear regression for mul”