Tail density estimation for exploratory data analysis using kernel methods-Reference-Cited by-同舟云学术

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Tail density estimation for exploratory data analysis using kernel methods

Published:2018-11-03 Issue:1 Volume:31 Page:144-174
ISSN:1048-5252
Container-title:Journal of Nonparametric Statistics
language:en
Short-container-title:Journal of Nonparametric Statistics

Author:

Béranger B.¹²^ORCID,Duong T.¹,Perkins-Kirkpatrick S. E.³^ORCID,Sisson S. A.²^ORCID

Affiliation:

1. Theoretical and Applied Statistics Laboratory (LSTA), University Pierre and Marie Curie - Paris 6, Paris, France

2. School of Mathematics and Statistics, University of New South Wales, Sydney, Australia

3. Climate Change Research Centre, University of New South Wales, Sydney, Australia

Funder

Australian Centre of Excellence for Climate System Science

Australian Research Council

Australian Centre of Excellence for Mathematical and Statistical Frontiers

ARC Discovery Project scheme

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10485252.2018.1537442

Reference75 articles.

1. On Bandwidth Variation in Kernel Estimates-A Square Root Law

2. Residual Life Time at Great Age

3. Statistics of Extremes

4. Beranger, B., and Padoan, S. (2015), ‘Extreme Dependence Models’, in Extreme Value Modeling and Risk Analysis, eds. Dipak K. Dey, and Jun Yan, New York: Chapman and Hall/CRC, pp. 325–352.

5. Quantitative Operational Risk Models

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