Name
Abstract | ||
|---|---|---|
The problem of inferring the distribution of a random vector given that its norm is large requires modeling a homogeneous limiting density. We suggest an approach based on graphical models which is suitable for high-dimensional vectors. We introduce the notion of one-component regular variation to describe a function that is regularly varying in its first component. We extend the representation and Karamata's theorem to one-component regularly varying functions, probability distributions and densities, and explain why these results are fundamental in multivariate extreme-value theory. We then generalize the Hammersley-Clifford theorem to relate asymptotic conditional independence to a factorization of the limiting density, and use it to model multivariate tails. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1017/jpr.2016.37 | JOURNAL OF APPLIED PROBABILITY |
Keywords | Field | DocType |
Regular variation,Karamata's theorem,homogeneous distribution,multivariate exceedances over threshold,graphical model | Combinatorics,Multivariate statistics,Homogeneous,Conditional independence,Probability distribution,Multivariate random variable,Factorization,Graphical model,Limiting,Mathematics | Journal |
Volume | Issue | ISSN |
53 | 3 | 0021-9002 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrien Hitz | 1 | 0 | 0.34 |
| R. J. Evans | 2 | 18 | 4.48 |