Statistical learning theory for fitting multimodal distribution to rainfall data: an application

DOI10.1080/02664763.2011.559210OpenAlexW2083124309MaRDI QIDQ5124935

Prajneshu, Himadri Ghosh

Publication date: 30 September 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2011.559210

zbMATH Keywords

statistical learning theoryextreme valuebootstrap techniquestructural risk minimization principlemultimodal rainfall distribution

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• ismev

Cites Work

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• Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems
• A moment estimator for the index of an extreme-value distribution
• A simple general approach to inference about the tail of a distribution
• Nonparametric tail estimation using a double bootstrap method.
• Accounting for the threshold uncertainity in extreme value estimation
• A Diagnostic for Selecting the Threshold in Extreme Value Analysis
• Bayesian analysis of extreme events with threshold estimation
• Necessary and Sufficient Conditions for the Uniform Convergence of Means to their Expectations
• Tail Index Estimation, Pareto Quantile Plots, and Regression Diagnostics
• Tail Index Estimation in Models of Generalized Order Statistics
• Nonparametric Analysis of Univariate Heavy‐Tailed Data
• An introduction to statistical modeling of extreme values

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