Local polynomial maximum likelihood estimation for Pareto-type distributions

By Jan Beirlant, Yuri Goegebeur. 2004

73 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a08df1b9b2d8aec6fc1f6631a5c0483fcbdaa327

How researchers see this paper

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

In Finance

1. Laurent Gardes
2. Stéphane Girard
3. Armelle Guillou
4. Antoine Schorgen
5. Abdelaati Daouia
6. Yuri Goegebeur
7. I. Van Keilegom
8. Gerda Claeskens

In Global and Planetary Change

1. Laurent Gardes
2. Stéphane Girard
3. Armelle Guillou
4. Antoine Schorgen
5. Yuri Goegebeur
6. Abdelaati Daouia
7. Gilles Stupfler
8. H. Wang

In Statistics and Probability

1. Laurent Gardes
2. Stéphane Girard
3. Armelle Guillou
4. Antoine Schorgen
5. Yuri Goegebeur
6. Abdelaati Daouia
7. Gerda Claeskens
8. I. Van Keilegom

Citations in the corpus

• Nonparametric regression estimation of conditional tails: the random covariate case (2014, methodology): “Fully nonparametric methods like local estimation and penalized likelihood estimation can be found in Davison and Ramesh,[7] Hall and Tajvidi,[8] Pauli and Coles,[9] Beirlant and Goegebeur,[10] Chavez-Demoulin and Davison,[11] Gardes and Girard [12,13] and Goegebeur and de Wet.”
• A moving window approach for nonparametric estimation of the conditional tail index (2008, background): “These results are extended in [5] where local polynomials estimators are proposed for multivariate covariates and where their asymptotic properties are established for very regular functions γ(x) (at least twice continuously differentiable).”
• Conditional extremes from heavy-tailed distributions: an application to the estimation of extreme rainfall return levels (2010, background): “These results are extended in (Beirlant and Goegebeur 2004) where local polynomial estimators are proposed for multidimensional covariates and where their asymptotic properties are established.”
• Bootstrap confidence bands for regression curves and their derivatives (2003, background): “Other interesting multiparameter models include the generalized extreme value and Pareto distributions; see, respectively, Davison and Ramesh (2000) and Beirlant and Goegebeur (2004) for application of local polynomial estimation.”
• On kernel smoothing for extremal quantile regression (2012, methodology): “The experiments indicate that q̂n(βn|x) is efficient relative to the modern smoothing estimator q̂GP n (βn|x) first introduced in [1, 2].”