Bayesian spline method for assessing extreme loads on wind turbines

DOI10.1214/13-AOAS670zbMath1283.62241arXiv1401.2760MaRDI QIDQ2441846

Eunshin Byon, Giwhyun Lee, Yu Ding, Lewis Ntaimo

Publication date: 28 March 2014

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

Full work available at URL: https://arxiv.org/abs/1401.2760

zbMATH Keywords

reliabilityMonte Carlo integrationwind powerBayesian spline regression

Mathematics Subject Classification ID

Numerical computation using splines (65D07) Bayesian inference (62F15) Applications of statistics in engineering and industry; control charts (62P30) Monte Carlo methods (65C05) Reliability and life testing (62N05)

Related Items (4)

Parameter calibration in wake effect simulation model with stochastic gradient descent and stratified sampling ⋮ Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models ⋮ Uncertainty quantification of stochastic simulation for black-box computer experiments ⋮ Nonparametric importance sampling for wind turbine reliability analysis with stochastic computer models

Uses Software

• ismev

Cites Work

• Unnamed Item
• Making and Evaluating Point Forecasts
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Multi-disciplinary constrained optimization of wind turbines
• Estimating the dimension of a model
• A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion
• An introduction to statistical modeling of extreme values

This page was built for publication: Bayesian spline method for assessing extreme loads on wind turbines