Local likelihood smoothing of sample extremes

By A. C. Davison, N. Ramesh. 2000

183 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/70d9832431f55b25b9bbd6f3a3937f29c5ba556f

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In Global and Planetary Change

In Ocean Engineering

Citations in the corpus

Nonparametric Analysis of Temporal Trend When Fitting Parametric Models to ExtremeValue Data (2000, methodology): “Independently of the work in the present paper, Davison and Ramesh (2000) have developed local likelihood-based methods for smoothing sample extremes.”
Generalized additive modelling of sample extremes (2005, background): “Davison and Ramesh (2000), Hall and Tajvidi (2000) and Ramesh and Davison (2002) have developed local likelihood models for smoothing sample extremes.”
On kernel smoothing for extremal quantile regression (2012, unknown): “A semi-parametric approach to modeling trends in sample extremes, based on local polynomial fitting of the Generalized extreme-value distribution, has been introduced in [8].”
Comparison of methods for non-stationary hydrologic frequency analysis: Case study using annual maximum daily precipitation in Taiwan (2017, background): “Although many researches have employed linear trends in parameter values to model non-stationarity, doubts remain as to whether these trends can be simplified into linear or simple functions (Hall and Tajvidi, 2000; Davison and Ramesh, 2000; Ramesh and Davison, 2002).”
Penalized likelihood inference in extreme value analyses (2001, methodology): “A similar study was made by Davison & Ramesh (2000) using the local likelihood methodology they propose.”
Statistics of extremes in hydrology (2002, methodology): “New samples could be generated through a multi-stage procedure: (i) convert the original data to residuals using Eq. (6) (with estimates for the model fit to the original data being substituted in place of the unknown parameters); (ii) produce new samples of residuals by the conventional bootstrap; ”
Geostatistics of extremes (2012, background): “Davison & Ramesh (2000), Hall & Tajvidi (2000), Proc.”
Threshold modeling of extreme spatial rainfall (2013, methodology): “Davison et al. [2012] found that max-stable models indeed provided better estimates of extreme spatial rainfall than did Bayesian and standard copula approaches. The copula approach of Salvadori and Michele [2010] is intended for a given network of gauge stations rather than for a truly spatial anal”
Bootstrap confidence bands for regression curves and their derivatives (2003, background): “value and Pareto distributions, see, respectively, Davison and Ramesh (2000) and Beirlant”
Conditional extremes from heavy-tailed distributions: an application to the estimation of extreme rainfall return levels (2010, background): “Fully non-parametric estimators have been first introduced in (Davison and Ramesh 2000), where a local polynomial modeling of the extreme observations is used.”
Statistical Modeling of Spatial Extremes (2012, methodology): “Other approaches to spatial smoothing might also be adopted, as in Butler et al. (2007), who use local likelihood estimation for extreme-value models (Davison and Ramesh (2000); Hall and Tajvidi (2000)), but they do not seem necessary here.”
Functional kernel estimators of large conditional quantiles (2011, unknown): “Fully nonparametric estimators have been first introduced in [9, 6] through local polynomial and spline models.”