Nonparametric Analysis of Temporal Trend When Fitting Parametric Models to Extreme­Value Data

By Peter Hall, N. Tajvidi. 2000

183 citations recorded by the source corpus.

Subfields: Environmental Engineering, Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/3cef89bc384ba1a2cac046eccced934e1a048815

How researchers see this paper

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

In Environmental Engineering

1. A. C. Davison
2. Emeric Thibaud
3. Mathieu Ribatet
4. Raphaël Huser
5. Raphaël Mutzner
6. Jonathan A. Tawn
7. Simone A. Padoan
8. John H. J. Einmahl

In Finance

1. A. C. Davison
2. Raphaël Huser
3. Mathieu Ribatet
4. Emeric Thibaud
5. Thomas Opitz
6. Stéphane Girard
7. Laurent Gardes
8. Simone A. Padoan

In Global and Planetary Change

1. A. C. Davison
2. Raphaël Huser
3. Mathieu Ribatet
4. Emeric Thibaud
5. Laurent Gardes
6. Stéphane Girard
7. Thomas Opitz
8. Simone A. Padoan

Citations in the corpus

• Statistics of extremes in hydrology (2002, unknown): “(9), with nonparametric alternatives being feasible [38,75].”
• Statistics of heteroscedastic extremes (2016, background): “Hall and Tajvidi (2000) estimate nonparametric trends in parameters of GPDs and general-4 ized extreme value distributions by locally parametrizing the trend.”
• A moving window approach for nonparametric estimation of the conditional tail index (2008, background): “More recently, Hall and Tajvidi [24] proposed to mix a non-parametric estimation of the trend with a parametric assumption on Y given x.”
• Modelling Time Series Extremes (2012, background): “Davison & Ramesh (2000), Hall & Tajvidi (2000), Ramesh & Davison (2002), Butler et al. (2007) and Süveges (2007) have applied this in different settings, including spatial extremal analysis for oceanography and timevarying estimation of the extremal index.”
• Penalized likelihood inference in extreme value analyses (2001, methodology): “One possibility is to use local likelihood techniques as independently proposed by Davison & Ramesh (2000), and Hall & Tajvidi (2000).”
• Estimation of the conditional tail index using a smoothed local Hill estimator (2014, methodology): “Smith [16] and Davison and Smith [7] considered a regression model while Hall and Tajvidi [12] used a semi-parametric approach to estimate the conditional tail index.”
• A dependence measure for multivariate and spatial extreme values: Properties and inference (2003, methodology): “For t=0, h@ A,t reduces to a variant of the Hall & Tajvidi (2000b) estimator that has been introduced to estimate the V function of a multivariate extreme-value distribution.”
• Conditional extremes from heavy-tailed distributions: an application to the estimation of extreme rainfall return levels (2010, methodology): “Parametric models for conditional extremes are proposed in (Davison and Smith 1990; Smith 1989) whereas semi-parametric methods are considered in (Beirlant and Goegebeur 2003, Hall and Tajvidi 2000).”
• Statistical Modelling of Spatial Extremes (2012, methodology): “(2007), who use local likelihood estimation for extreme-value models (Davison and Ramesh, 2000; Hall and Tajvidi, 2000), but they and do not seem necessary here.”
• Estimation of Extreme Conditional Quantiles Through Power Transformation (2013, background): “One class of work models extremes by fitting a fully parametric model, such as generalized extreme value distribution or generalized Pareto distribution (GPD), where the location, shape and scale parameters are allowed to depend on covariates either parametrically or nonparametrically (Davison and R”
• Likelihood-Based Local Linear Estimation of the Conditional Variance Function (2004, methodology): “We join with Hall and Tajvidi (2000) in proposing the natural way of overcoming this dif culty by modeling logv.x/ as a polynomial locally.”
• Geostatistics of extremes (2012, background): “Davison & Ramesh (2000), Hall & Tajvidi (2000), Proc.”
• On kernel smoothing for extremal quantile regression (2012, background): “Hall and Tajvidi [20] suggested a nonparametric estimation of the temporal trend when fitting parametric models to extreme values.”
• Kernel estimators of extreme level curves (2011, methodology): “Parametric models are proposed in [11, 39] whereas semi-parametric methods are considered in [3, 29].”
• A hierarchical model for the analysis of spatial rainfall extremes (2007, background): “Several review articles have remarked on the usefulness of extreme value theory in assessing climate changes (Hall and Tajvidi 2000; Piegorsch et al. 1998; Katz et al. 2002), but some problems arise when we deal with time series of extreme values in a certain area.”
• Functional kernel estimators of large conditional quantiles (2011, methodology): “Parametric models are introduced in [10, 42] and semi-parametric methods are considered in [2, 34].”