BOUNDS ON BIVARIATE DISTRIBUTION FUNCTIONS WITH GIVEN MARGINS AND MEASURES OF ASSOCIATION

By Roger B. Nelsen, J. Quesada-Molina, Joseé Antonio Rodriíguez-Lallena, Manuel Úbeda-Flores. 2001

69 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/99046dad22aaf8610ad037f67e53df205ec76c13

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

1. Andrew J. Patton
2. Yanqin Fan
3. Sang Soo Park
4. Fabrizio Durante
5. Carlo Sempi

In Management Science and Operations Research

1. Fabrizio Durante
2. Yanqin Fan
3. Sang Soo Park
4. Roger B. Nelsen
5. Roger J. A. Laeven
6. Carlo Sempi
7. Rob Kaas
8. Andrew J. Patton

In Statistics and Probability

1. Andrew J. Patton
2. Yanqin Fan
3. Sang Soo Park
4. Fabrizio Durante
5. Carlo Sempi
6. Roger B. Nelsen
7. Roger J. A. Laeven
8. Rob Kaas

Citations in the corpus

• Improved Fréchet Bounds and Model-Free Pricing of Multi-Asset Options (2010, unknown): “[10] computed the improved bounds when a measure of association, such as Kendall’s τ or Spearman’s ρ, is given, and the Bertino’s family of copulas [2] yields best-possible bounds when the values of the copula on the main diagonal are known.”
• Best-possible bounds on sets of bivariate distribution functions (2004, background): “1) are themselves bivariate distribution functions with margins F and G: In an earlier paper [10], the authors found bounds on the set of joint distribution functions of continuous random variables with known margins and a known value of a measure of association such as Kendall’s tau or Spearman’s r”
• PARTIAL IDENTIFICATION OF THE DISTRIBUTION OF TREATMENT EFFECTS AND ITS CONFIDENCE SETS (2009, methodology): “If instead of Kendall’s t, the value of Spearman’s r between the potential outcomes is known, one can also establish tighter bounds on FD(z) by using Theorem 4 in Nelsen et al. (2001) and Lemma 6.”
• Worst VaR scenarios with given marginals and measures of association (2009, methodology): “To simulate from Cτ (see (11) of Nelsen et al. (2001)) and Cτ the following algorithm is adopted: 1.”
• Copulas in Econometrics (2014, background): “Nelsen & Ubeda-Flores (2004) and Nelsen et al. (2001, 2004) establish improved Fréchet-Hoeffding bounds when such partial dependence information is available.”
• Copula Theory: An Introduction (2010, background): “’s (see [15, 153, 168]), especially when some additional information is given, like lower dimensional marginals [37, 45, 103], and measures of association [151, 155].”