Vector generalized linear and additive extreme value models

DOI10.1007/s10687-007-0032-4zbMath1150.62371OpenAlexW1989345832MaRDI QIDQ928489

Thomas W. Yee, Alec G. Stephenson

Publication date: 18 June 2008

Published in: Extremes (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10687-007-0032-4

zbMATH Keywords

iteratively reweighted least squarespenalized likelihoodR softwarevector splines

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Generalized linear models (logistic models) (62J12) Statistics of extreme values; tail inference (62G32)

Related Items

Spatial extreme value analysis to project extremes of large‐scale indicators for severe weather ⋮ Efficient estimation of partially linear tail index models using B‐splines ⋮ Two-parameter link functions, with applications to negative binomial, Weibull and quantile regression ⋮ A modeler's guide to extreme value software ⋮ A software review for extreme value analysis ⋮ Semiparametric regression during 2003--2007 ⋮ Mixed model-based additive models for sample extremes ⋮ Smoothing sample extremes: the mixed model approach ⋮ Synoptic airflow and UK daily precipitation extremes: development and validation of a vector generalized linear model ⋮ SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS ⋮ GENERALIZED EXTREME VALUE ADDITIVE MODEL ANALYSIS VIA MEAN FIELD VARIATIONAL BAYES ⋮ Bivariate extreme analysis of Olympic swimming data ⋮ Modelling the clustering of extreme events for short-term risk assessment ⋮ Regression-type analysis for multivariate extreme values ⋮ Statistical Regression Analysis of Threshold Excesses with Systematically Missing Covariates ⋮ On tail trend detection: modeling relative risk

Uses Software

• R
• VGAM
• mathStatica
• VaR
• ismev
• evd

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Linear smoothers and additive models
• Software for the analysis of extreme events: The current state and future directions
• Extremes and related properties of random sequences and processes
• Statistical inference using extreme order statistics
• A practical guide to splines
• Smoothing by spline functions.
• Reduced-rank vector generalized linear models
• Maximum likelihood estimation in a class of nonregular cases
• Parameter and Quantile Estimation for the Generalized Pareto Distribution
• Generalized Additive Models: Some Applications
• Bivariate extreme value theory: Models and estimation
• Statistics for near independence in multivariate extreme values
• Fisher Information for a Bivariate Extreme Value Distribution
• Estimation of Parameters and Larger Quantiles Based on the k Largest Observations
• Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information
• Penalized likelihood inference in extreme value analyses
• Inference for Clusters of Extreme Values
• Fisher information for a multivariate extreme value distribution
• The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations
• Generalized Additive Modelling of Sample Extremes
• An introduction to statistical modeling of extreme values