Covariate Adjusted Correlation Analysis via Varying Coefficient Models
By Damla Sentürk, H. Muller. 2005
99 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/ddb850e14c35eae1f413a7bca3f0191ff56f05e9
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
- H. Muller
- Jeng-Min Chiou
- Jane-ling Wang
- Jianqing Fan
- Wenyang Zhang
- Fang Yao
- Jian Qing Shi
- Piotr Kokoszka
In Finance
- Jianqing Fan
- H. Muller
- Jeng-Min Chiou
- Wenyang Zhang
- Jane-ling Wang
- Jiancheng Jiang
- S. Vaughan
- H. ller
In Statistics and Probability
- H. Muller
- Jeng-Min Chiou
- Jane-ling Wang
- Jianqing Fan
- Wenyang Zhang
- Fang Yao
- Yingcun Xia
- Damla Sentürk
Citations in the corpus
- Statistical Methods with Varying Coefficient Models. (2008, methodology): “For example, Sentürk and Müller (2005) applied the varying coefficient models in covariate adjusted correlation analysis.”
- Functional Data Analysis (2016, methodology): “For regression-based functional classification models, functional generalized linear models (James 2002; M¨uller 2005; M¨uller & Stadtm¨uller 2005) or more specifically, functional binary regression, such as functional logistic regression, are popular approaches.”