• Corpus ID: 247411390

# Single-index models for extreme value index regression

```@inproceedings{Yoshida2022SingleindexMF,
title={Single-index models for extreme value index regression},
author={Takuma Yoshida},
year={2022}
}```
Since the extreme value index (EVI) controls the tail behaviour of the distribution function, the estimation of EVI is a very important topic in extreme value theory. Recent developments in the estimation of EVI along with covariates have been in the context of nonparametric regression. However, for the large dimension of covariates, the fully nonparametric estimator faces the problem of the curse of dimensionality. To avoid this, we apply the single index model to EVI regression under Pareto…

## References

SHOWING 1-10 OF 39 REFERENCES

• Mathematics
Metrika
• 2019
The tail index is an important parameter in the whole of extreme value theory. In this article, we consider the estimation of the tail index in the presence of a random covariate, where the
• Mathematics
• 2016
Single index models offer greater flexibility in data analysis than linear models but retain some of the desirable properties such as the interpretability of the coefficients. We consider a
• Mathematics
• 2009
In extreme value statistics, the tail index is an important measure to gauge the heavy-tailed behavior of a distribution. Under Pareto-type distributions, we employ the logarithmic function to link
• Mathematics
• 2009
For the past two decades, the single-index model, a special case of pro- jection pursuit regression, has proven to be an efficient way of coping with the high-dimensional problem in nonparametric
• Mathematics
• 2020
It is important to quantify and predict rare events that have huge societal effects. Existing works on analyzing such events mainly rely on either inflexible parametric models or nonparametric models
• Mathematics, Economics
Statistica Sinica
• 2012
Under mild conditions, it is shown that the simple linear quantile regression offers a consistent estimate of the index parameter vector, and the resulting estimator of the quantile function performs asymptotically as efficiently as if the true value of theindex vector were known.
• Mathematics
• 2014
We present families of nonparametric estimators for the conditional tail index of a Pareto-type distribution in the presence of random covariates. These families are constructed from locally weighted
• Mathematics, Computer Science
Bernoulli
• 2020
A method to compute the penalized least squares estimators (PLSEs) of the parametric and the nonparametric components given independent and identically distributed (i.i.d.) data is developed and it is proved the consistency and the rates of convergence of the estimators.
• Mathematics, Computer Science
J. Multivar. Anal.
• 2010
• Mathematics
Journal of Business & Economic Statistics
• 2020
Abstract–Understanding why extreme events occur is often of major scientific interest in many fields. The occurrence of these events naturally depends on explanatory variables, but there is a severe