Copula-based regression models: A survey

By Nikolai Kolev, D. Paiva. 2009

79 citations recorded by the source corpus.

Subfields: Demography, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5130406744604e8eaee608218c0beb92630f5fcd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. Frank Xing
2. Edward W. Frees
3. Lu Yang
4. Gee Y. Lee
5. Roy E. Welsch
6. G. Pegram
7. András Bárdossy
8. Erik Cambria

In Finance

1. Lu Yang
2. Gee Y. Lee
3. Frank Xing
4. Edward W. Frees
5. Roy E. Welsch
6. G. Pegram
7. András Bárdossy
8. Erik Cambria

In Management Science and Operations Research

1. Lu Yang
2. Gee Y. Lee
3. Frank Xing
4. Edward W. Frees
5. Roy E. Welsch
6. G. Pegram
7. András Bárdossy
8. Erik Cambria

Citations in the corpus

• Natural language based financial forecasting: a survey (2017, methodology): “Although there are other models, such as copula-based regression [56] or structural equation modeling (SEM), that are capable of capturing this correlation, VAR is still”
• Multivariate Frequency-Severity Regression Models in Insurance (2016, background): “In more general cases, [20] provides a detailed introduction of copula regression that focuses on the Gaussian copula and [21] surveys copula regression applications.”
• Interpolation of precipitation under topographic influence at different time scales (2013, methodology): “[38] Besides the classical linear or linearized regression between elevation and precipitation, one might use copula regression as suggested by Kolev and Paiva [2009]. This form of detrending would also be possible, but interpolation of the residuals would lead to problems B ARDOSSY AND PEGRAM: INTE”