Flexible Copula Density Estimation with Penalized Hierarchical B‐splines

By Goeran Kauermann, Christian Schellhase, David Ruppert. 2013

74 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/39cb9d9bdd5eb2b0f75a5b20313bccb5e1dbe4d6

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Brian D. Marx
2. Paul H. C. Eilers
3. Arthur Charpentier
4. Gery Geenens
5. María Durbán
6. Davy Paindaveine
7. Zhilin Zhang
8. Damien Garcia

In Finance

1. Arthur Charpentier
2. Gery Geenens
3. Davy Paindaveine
4. Fabrizio Durante
5. Piotr Jaworski

In Statistics and Probability

1. María Durbán
2. Paul H. C. Eilers
3. Brian D. Marx
4. Arthur Charpentier
5. Gery Geenens
6. Davy Paindaveine
7. Marc Hallin
8. Thomas Verdebout

Citations in the corpus

• Twenty years of P-splines (2015, background): “We already mentioned one- and two-dimensional log-concave densities in Section 3.. Kauermann et al. (2013) explored flexible copula density estimation.”
• Probit Transformation for Nonparametric Kernel Estimation of the Copula Density (2014, methodology): “…and Gijbels and Mielniczuk (1990), and arguably remains very attractive compared to its competitors, such as splines smoothing (Shen et al, 2008, Kauermann et al, 2013), wavelets (Hall and Neumeyer, 2006, Genest et al, 2009b, Autin et al, 2010), Bernstein polynomials (Bouezmarni et al, 2010,…”