High Risk Scenarios and Extremes

A geometric approach

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	Frontmatter, Foreword, AcknowledgementsDownload pp. i–viii
	ContentsDownload pp. ix–xiii
	IntroductionDownload pp. 1–11
	PreviewDownload pp. 13–39
I	Point Processesp. 41
1	An intuitive approachpp. 41–47
2	Poisson point processespp. 48–63
3	The distributionpp. 63–69
4	Convergencepp. 69–81
5	Converging sample cloudspp. 81–99
II	Maximap. 100
6	The univariate theory: maxima and exceedancespp. 100–110
7	Componentwise maximapp. 110–122
III	High Risk Limit Lawsp. 123
8	High risk scenariospp. 123–135
9	The Gauss-exponential domain, rotund setspp. 135–147
10	The Gauss-exponential domain, unimodal distributionspp. 147–156
11	Flat functions and flat measurespp. 156–169
12	Heavy tails and bounded vectorspp. 170–176
13	The multivariate GPDspp. 176–181
IV	Thresholdsp. 182
14	Exceedances over horizontal thresholdspp. 183–211
15	Horizontal thresholds – examplespp. 211–230
16	Heavy tails and elliptic thresholdspp. 230–263
17	Heavy tails – examplespp. 263–295
18	Regular variation and excess measurespp. 295–347
	Open problemsp. 348
19	The stochastic modelpp. 349–356
20	The statistical analysispp. 356–360
	Bibliographypp. 361–368
	Indexpp. 369–375