Empirical likelihood for small area estimation
By Sanjay Chaudhuri, Malay Ghosh. 2011
51 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/b0b035618751c27c2a816796b6b0f68a41c4ddd9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Management Science and Operations Research
- Danny Pfeffermann
- Teng Yin
- Debashis Mondal
- Sanjay Chaudhuri
- J. Rao
- Isabel Molina
- Domingo Morales
- Balgobin Nandram
In Statistics and Probability
- Danny Pfeffermann
- Teng Yin
- Debashis Mondal
- Sanjay Chaudhuri
- R. Tiller
- Gauri Sankar Datta
- Malay Ghosh
- Partha Lahiri
Citations in the corpus
- Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation (2017, methodology): “For example, Rao and Wu (2010) applied Bayesian EL methods to survey sampling, Chaudhuri and Ghosh (2011) to small area estimation, Yang and He (2012) to quantile regression and Mengersen et al.”
- New important developments in small area estimation (2013, methodology): “Use of empirical likelihood in Bayesian inference Chaudhuri and Ghosh (2011) consider the use of empirical likelihoods (EL) instead of fully parametric likelihoods as another way of robustifying the inference.”