Prediction regions for bivariate extreme events
(2004) In Australian & New Zealand Journal of Statistics 46(1). p.99-112- Abstract
- This paper suggests using a mixture of parametric and non-parametric methods to construct prediction regions in bivariate extreme-value problems. The non-parametric part of the technique is used to estimate the dependence function, or copula, and the parametric part is employed to estimate the marginal distributions. A bootstrap calibration argument is suggested for reducing coverage error. This combined approach is compared with a more parametric one, relative to which it has the advantages of being more flexible and simpler to implement. It also enjoys these features relative to predictive likelihood methods. The paper shows how to construct both compact and semi-infinite bivariate prediction regions, and it treats the problem of... (More)
- This paper suggests using a mixture of parametric and non-parametric methods to construct prediction regions in bivariate extreme-value problems. The non-parametric part of the technique is used to estimate the dependence function, or copula, and the parametric part is employed to estimate the marginal distributions. A bootstrap calibration argument is suggested for reducing coverage error. This combined approach is compared with a more parametric one, relative to which it has the advantages of being more flexible and simpler to implement. It also enjoys these features relative to predictive likelihood methods. The paper shows how to construct both compact and semi-infinite bivariate prediction regions, and it treats the problem of predicting the value of one component conditional on the other. The methods are illustrated by application to Australian annual maximum temperature data. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/281712
- author
- Hall, P and Tajvidi, Nader LU
- organization
- publishing date
- 2004
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- likelihood, smoothing parameter, spline, predictive, non-parametric curve estimation, dependence function, cross-validation, copula, convex hull, bootstrap, calibration
- in
- Australian & New Zealand Journal of Statistics
- volume
- 46
- issue
- 1
- pages
- 99 - 112
- publisher
- Wiley-Blackwell
- external identifiers
-
- wos:000220783400012
- scopus:1842815877
- ISSN
- 1467-842X
- DOI
- 10.1111/j.1467-842X.2004.00316.x
- language
- English
- LU publication?
- yes
- id
- 73d99ba9-870e-4cd8-a850-b1cb984e2b61 (old id 281712)
- date added to LUP
- 2016-04-01 16:22:11
- date last changed
- 2022-01-28 19:12:18
@article{73d99ba9-870e-4cd8-a850-b1cb984e2b61, abstract = {{This paper suggests using a mixture of parametric and non-parametric methods to construct prediction regions in bivariate extreme-value problems. The non-parametric part of the technique is used to estimate the dependence function, or copula, and the parametric part is employed to estimate the marginal distributions. A bootstrap calibration argument is suggested for reducing coverage error. This combined approach is compared with a more parametric one, relative to which it has the advantages of being more flexible and simpler to implement. It also enjoys these features relative to predictive likelihood methods. The paper shows how to construct both compact and semi-infinite bivariate prediction regions, and it treats the problem of predicting the value of one component conditional on the other. The methods are illustrated by application to Australian annual maximum temperature data.}}, author = {{Hall, P and Tajvidi, Nader}}, issn = {{1467-842X}}, keywords = {{likelihood; smoothing parameter; spline; predictive; non-parametric curve estimation; dependence function; cross-validation; copula; convex hull; bootstrap; calibration}}, language = {{eng}}, number = {{1}}, pages = {{99--112}}, publisher = {{Wiley-Blackwell}}, series = {{Australian & New Zealand Journal of Statistics}}, title = {{Prediction regions for bivariate extreme events}}, url = {{http://dx.doi.org/10.1111/j.1467-842X.2004.00316.x}}, doi = {{10.1111/j.1467-842X.2004.00316.x}}, volume = {{46}}, year = {{2004}}, }