Effects of data dimension on empirical likelihood
By Songxi Chen, Liang Peng, Yingli Qin. 2009
108 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Mathematical Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/ff0c9d230289a6659747d80fd07c25aaa8ebdd35
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
- Chenlei Leng
- S. Mukhopadhyay
- Cheng Yong Tang
- Soumendra Nath Lahiri
- Manfred Mudelsee
- Michael Wolf
- Anders Bredahl Kock
- A. Chatterjee
In Mathematical Physics
In Statistics and Probability
- Cheng Yong Tang
- Chenlei Leng
- S. Mukhopadhyay
- Soumendra Nath Lahiri
- Heng Lian
- Yingcun Xia
- Jing Lei
- Songxi Chen
Citations in the corpus
- Jackknife Empirical Likelihood Test for Equality of Two High Dimensional Means (2013, unknown): “For comparisons, we prove the Wilks Theorem for the proposed jackknife empirical likelihood test under a model considered by Bai and Saranadasa (1996), Chen, Peng, and Qin (2009), and Chen and Qin (2010): B (Factor model).”
- Penalized empirical likelihood and growing dimensional general estimating equations (2012, background): “In high dimensional empirical likelihood, the magnitude of||λθ|| is no longerOp(n−1/2) as in the fixed dimensional case (Hjort et al., 2009; Chen et al., 2009).”
- A penalized empirical likelihood method in high dimensions (2013, background): “Hjort, McKeague and Van Keilegom (2009) and Chen et al. (2009) proved a version of the result (i.”