Estimation and uncertainty quantification for extreme quantile regions
نویسندگان
چکیده
منابع مشابه
Extreme Quantile Estimation for Dependent Data with Applications to Finance
The asymptotic normality of a class of estimators for extreme quantiles is established under mild structural conditions on the observed stationary β–mixing time series. Consistent estimators of the asymptotic variance are introduced, which render possible the construction of asymptotic confidence intervals for the extreme quantiles. Moreover, it is shown that many well-known time series models ...
متن کاملExtreme quantile estimation with nonparametric adaptive importance sampling
In this article, we propose a nonparametric adaptive importance sampling (NAIS) algorithm to estimate rare event quantile. Indeed, Importance Sampling (IS) is a well-known adapted random simulation technique. It consists in generating random weighted samples from an auxiliary distribution rather than the distribution of interest. The optimization of this auxiliary distribution is often very dif...
متن کاملForward and Backward Uncertainty Quantification in Optimization
This contribution gathers some of the ingredients presented during the Iranian Operational Research community gathering in Babolsar in 2019.It is a collection of several previous publications on how to set up an uncertainty quantification (UQ) cascade with ingredients of growing computational complexity for both forward and reverse uncertainty propagation.
متن کاملError Estimation and Uncertainty Quantification for non-linear CFD problems
Modern research and engineering rely on numerical simulations to predict the behaviour of fluids and some derived physical quantities of interest. These predictions are often strewn with errors and uncertainties. Numerical errors come from replacing the real physics with approximate models solved by numerical approximations, while uncertainties are due to insufficient knowledge of some input va...
متن کاملParameter estimation and uncertainty quantification for an epidemic model.
We examine estimation of the parameters of Susceptible-Infective-Recovered (SIR) models in the context of least squares. We review the use of asymptotic statistical theory and sensitivity analysis to obtain measures of uncertainty for estimates of the model parameters and the basic reproductive number (R0)---an epidemiologically significant parameter grouping. We find that estimates of differen...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Extremes
سال: 2019
ISSN: 1386-1999,1572-915X
DOI: 10.1007/s10687-019-00364-0