Regular variation of a random length sequence of random variables and application to risk assessment View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-03

AUTHORS

Charles Tillier, Olivier Wintenberger

ABSTRACT

When assessing risks on a finite-time horizon, the problem can often be reduced to the study of a random sequence C(N) = (C1,…,CN) of random length N, where C(N) comes from the product of a matrix A(N) of random size N × N and a random sequence X(N) of random length N. Our aim is to build a regular variation framework for such random sequences of random length, to study their spectral properties and, subsequently, to develop risk measures. In several applications, many risk indicators can be expressed from the extremal behavior of ∥C(N)∥, for some norm ∥⋅∥. We propose a generalization of Breiman’s Lemma that gives way to a tail estimate of ∥C(N)∥ and provides risk indicators such as the ruin probability and the tail index for Shot Noise Processes on a finite-time horizon. Lastly, we apply our main result to a model used in dietary risk assessment and in non-life insurance mathematics to illustrate the applicability of our method. More... »

PAGES

27-56

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10687-017-0297-1

DOI

http://dx.doi.org/10.1007/s10687-017-0297-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090804366


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Laboratoire de Statistique Th\u00e9orique et Appliqu\u00e9e", 
          "id": "https://www.grid.ac/institutes/grid.463964.a", 
          "name": [
            "Universit\u00e9 Paris Nanterre, 200 avenue de la r\u00e9publique, 92000, Nanterre, France", 
            "Universit\u00e9 Pierre et Marie Curie, LSTA, 4 place Jussieu, 75005, Paris, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tillier", 
        "givenName": "Charles", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Copenhagen", 
          "id": "https://www.grid.ac/institutes/grid.5254.6", 
          "name": [
            "Universit\u00e9 Pierre et Marie Curie, LSTA, 4 place Jussieu, 75005, Paris, France", 
            "Department of Mathematical Sciences, UCPH, Universitetsparken 5, 2100, Copenhague, Danemark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wintenberger", 
        "givenName": "Olivier", 
        "id": "sg:person.013144244757.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013144244757.26"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.2298/pim0694121h", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005482610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4613-9058-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009744271", 
          "https://doi.org/10.1007/978-1-4613-9058-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4613-9058-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009744271", 
          "https://doi.org/10.1007/978-1-4613-9058-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0304-4149(94)90113-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009783717"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.spa.2008.05.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009825890"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1012537231", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1012537231", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3390/risks2010003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012694648"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11009-011-9274-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030348678", 
          "https://doi.org/10.1007/s11009-011-9274-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/17513750903222960", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031465727"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1025148622954", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038462494", 
          "https://doi.org/10.1023/a:1025148622954"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0304-4149(01)00156-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044557109"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.spl.2014.01.026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047175045"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2298/pim0694171j", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050789571"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-02303-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051840937", 
          "https://doi.org/10.1007/978-3-319-02303-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-02303-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051840937", 
          "https://doi.org/10.1007/978-3-319-02303-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/0331041", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062844447"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/14-ps231", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064394817"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aoap/1031863174", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064397658"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aoap/1177005071", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064397974"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aop/1176991767", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064404212"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1239/aap/1354716592", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064441085"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1239/jap/1082552199", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064441848"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2143/ast.36.2.2017926", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069075450"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3150/15-bej699", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071057104"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3934/mbe.2008.5.35", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071741471"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/cbo9780511721434", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098702033"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/7431", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098867659"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/9780470316962", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109489376"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1109489376", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-03", 
    "datePublishedReg": "2018-03-01", 
    "description": "When assessing risks on a finite-time horizon, the problem can often be reduced to the study of a random sequence C(N) = (C1,\u2026,CN) of random length N, where C(N) comes from the product of a matrix A(N) of random size N \u00d7 N and a random sequence X(N) of random length N. Our aim is to build a regular variation framework for such random sequences of random length, to study their spectral properties and, subsequently, to develop risk measures. In several applications, many risk indicators can be expressed from the extremal behavior of \u2225C(N)\u2225, for some norm \u2225\u22c5\u2225. We propose a generalization of Breiman\u2019s Lemma that gives way to a tail estimate of \u2225C(N)\u2225 and provides risk indicators such as the ruin probability and the tail index for Shot Noise Processes on a finite-time horizon. Lastly, we apply our main result to a model used in dietary risk assessment and in non-life insurance mathematics to illustrate the applicability of our method.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10687-017-0297-1", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1047855", 
        "issn": [
          "1386-1999", 
          "1572-915X"
        ], 
        "name": "Extremes", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "21"
      }
    ], 
    "name": "Regular variation of a random length sequence of random variables and application to risk assessment", 
    "pagination": "27-56", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "e7c729951b93033f3c01bef06193d764459550fab8a7f0efbabaa68a9f184fcd"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10687-017-0297-1"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1090804366"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10687-017-0297-1", 
      "https://app.dimensions.ai/details/publication/pub.1090804366"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T10:31", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000349_0000000349/records_113650_00000004.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs10687-017-0297-1"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10687-017-0297-1'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10687-017-0297-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10687-017-0297-1'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10687-017-0297-1'


 

This table displays all metadata directly associated to this object as RDF triples.

155 TRIPLES      21 PREDICATES      54 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10687-017-0297-1 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author N520bf3fbb9384accb7a91f0ee4e41b61
4 schema:citation sg:pub.10.1007/978-1-4613-9058-9
5 sg:pub.10.1007/978-3-319-02303-8
6 sg:pub.10.1007/s11009-011-9274-3
7 sg:pub.10.1023/a:1025148622954
8 https://app.dimensions.ai/details/publication/pub.1012537231
9 https://app.dimensions.ai/details/publication/pub.1109489376
10 https://doi.org/10.1002/9780470316962
11 https://doi.org/10.1016/0304-4149(94)90113-9
12 https://doi.org/10.1016/j.spa.2008.05.004
13 https://doi.org/10.1016/j.spl.2014.01.026
14 https://doi.org/10.1016/s0304-4149(01)00156-9
15 https://doi.org/10.1017/cbo9780511721434
16 https://doi.org/10.1080/17513750903222960
17 https://doi.org/10.1137/0331041
18 https://doi.org/10.1142/7431
19 https://doi.org/10.1214/14-ps231
20 https://doi.org/10.1214/aoap/1031863174
21 https://doi.org/10.1214/aoap/1177005071
22 https://doi.org/10.1214/aop/1176991767
23 https://doi.org/10.1239/aap/1354716592
24 https://doi.org/10.1239/jap/1082552199
25 https://doi.org/10.2143/ast.36.2.2017926
26 https://doi.org/10.2298/pim0694121h
27 https://doi.org/10.2298/pim0694171j
28 https://doi.org/10.3150/15-bej699
29 https://doi.org/10.3390/risks2010003
30 https://doi.org/10.3934/mbe.2008.5.35
31 schema:datePublished 2018-03
32 schema:datePublishedReg 2018-03-01
33 schema:description When assessing risks on a finite-time horizon, the problem can often be reduced to the study of a random sequence C(N) = (C1,…,CN) of random length N, where C(N) comes from the product of a matrix A(N) of random size N × N and a random sequence X(N) of random length N. Our aim is to build a regular variation framework for such random sequences of random length, to study their spectral properties and, subsequently, to develop risk measures. In several applications, many risk indicators can be expressed from the extremal behavior of ∥C(N)∥, for some norm ∥⋅∥. We propose a generalization of Breiman’s Lemma that gives way to a tail estimate of ∥C(N)∥ and provides risk indicators such as the ruin probability and the tail index for Shot Noise Processes on a finite-time horizon. Lastly, we apply our main result to a model used in dietary risk assessment and in non-life insurance mathematics to illustrate the applicability of our method.
34 schema:genre research_article
35 schema:inLanguage en
36 schema:isAccessibleForFree true
37 schema:isPartOf N10a25049a91d4a029f0cb1e05dbd289d
38 N8f72f92a97a548d98b48cf945cbfbf14
39 sg:journal.1047855
40 schema:name Regular variation of a random length sequence of random variables and application to risk assessment
41 schema:pagination 27-56
42 schema:productId N00bd2c6c144f46e0b186d0970dddae88
43 N05a2b9d0759b484c8c056cf01bfc67a6
44 Nad161bbbeb5a4c31906db17f09ef707b
45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090804366
46 https://doi.org/10.1007/s10687-017-0297-1
47 schema:sdDatePublished 2019-04-11T10:31
48 schema:sdLicense https://scigraph.springernature.com/explorer/license/
49 schema:sdPublisher N9d86df35008b4686957656420d498dfa
50 schema:url https://link.springer.com/10.1007%2Fs10687-017-0297-1
51 sgo:license sg:explorer/license/
52 sgo:sdDataset articles
53 rdf:type schema:ScholarlyArticle
54 N00bd2c6c144f46e0b186d0970dddae88 schema:name readcube_id
55 schema:value e7c729951b93033f3c01bef06193d764459550fab8a7f0efbabaa68a9f184fcd
56 rdf:type schema:PropertyValue
57 N05a2b9d0759b484c8c056cf01bfc67a6 schema:name doi
58 schema:value 10.1007/s10687-017-0297-1
59 rdf:type schema:PropertyValue
60 N10a25049a91d4a029f0cb1e05dbd289d schema:issueNumber 1
61 rdf:type schema:PublicationIssue
62 N3c25a8ff13804b16b9ed0c88380eb1cb schema:affiliation https://www.grid.ac/institutes/grid.463964.a
63 schema:familyName Tillier
64 schema:givenName Charles
65 rdf:type schema:Person
66 N520bf3fbb9384accb7a91f0ee4e41b61 rdf:first N3c25a8ff13804b16b9ed0c88380eb1cb
67 rdf:rest N8179956ffe9b405ba28dc84a6c1bdd11
68 N8179956ffe9b405ba28dc84a6c1bdd11 rdf:first sg:person.013144244757.26
69 rdf:rest rdf:nil
70 N8f72f92a97a548d98b48cf945cbfbf14 schema:volumeNumber 21
71 rdf:type schema:PublicationVolume
72 N9d86df35008b4686957656420d498dfa schema:name Springer Nature - SN SciGraph project
73 rdf:type schema:Organization
74 Nad161bbbeb5a4c31906db17f09ef707b schema:name dimensions_id
75 schema:value pub.1090804366
76 rdf:type schema:PropertyValue
77 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
78 schema:name Mathematical Sciences
79 rdf:type schema:DefinedTerm
80 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
81 schema:name Statistics
82 rdf:type schema:DefinedTerm
83 sg:journal.1047855 schema:issn 1386-1999
84 1572-915X
85 schema:name Extremes
86 rdf:type schema:Periodical
87 sg:person.013144244757.26 schema:affiliation https://www.grid.ac/institutes/grid.5254.6
88 schema:familyName Wintenberger
89 schema:givenName Olivier
90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013144244757.26
91 rdf:type schema:Person
92 sg:pub.10.1007/978-1-4613-9058-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009744271
93 https://doi.org/10.1007/978-1-4613-9058-9
94 rdf:type schema:CreativeWork
95 sg:pub.10.1007/978-3-319-02303-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051840937
96 https://doi.org/10.1007/978-3-319-02303-8
97 rdf:type schema:CreativeWork
98 sg:pub.10.1007/s11009-011-9274-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030348678
99 https://doi.org/10.1007/s11009-011-9274-3
100 rdf:type schema:CreativeWork
101 sg:pub.10.1023/a:1025148622954 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038462494
102 https://doi.org/10.1023/a:1025148622954
103 rdf:type schema:CreativeWork
104 https://app.dimensions.ai/details/publication/pub.1012537231 schema:CreativeWork
105 https://app.dimensions.ai/details/publication/pub.1109489376 schema:CreativeWork
106 https://doi.org/10.1002/9780470316962 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109489376
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1016/0304-4149(94)90113-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009783717
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1016/j.spa.2008.05.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009825890
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1016/j.spl.2014.01.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047175045
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1016/s0304-4149(01)00156-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044557109
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1017/cbo9780511721434 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098702033
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1080/17513750903222960 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031465727
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1137/0331041 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062844447
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1142/7431 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098867659
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1214/14-ps231 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064394817
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1214/aoap/1031863174 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064397658
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1214/aoap/1177005071 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064397974
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1214/aop/1176991767 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064404212
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1239/aap/1354716592 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064441085
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1239/jap/1082552199 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064441848
135 rdf:type schema:CreativeWork
136 https://doi.org/10.2143/ast.36.2.2017926 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069075450
137 rdf:type schema:CreativeWork
138 https://doi.org/10.2298/pim0694121h schema:sameAs https://app.dimensions.ai/details/publication/pub.1005482610
139 rdf:type schema:CreativeWork
140 https://doi.org/10.2298/pim0694171j schema:sameAs https://app.dimensions.ai/details/publication/pub.1050789571
141 rdf:type schema:CreativeWork
142 https://doi.org/10.3150/15-bej699 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071057104
143 rdf:type schema:CreativeWork
144 https://doi.org/10.3390/risks2010003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012694648
145 rdf:type schema:CreativeWork
146 https://doi.org/10.3934/mbe.2008.5.35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071741471
147 rdf:type schema:CreativeWork
148 https://www.grid.ac/institutes/grid.463964.a schema:alternateName Laboratoire de Statistique Théorique et Appliquée
149 schema:name Université Paris Nanterre, 200 avenue de la république, 92000, Nanterre, France
150 Université Pierre et Marie Curie, LSTA, 4 place Jussieu, 75005, Paris, France
151 rdf:type schema:Organization
152 https://www.grid.ac/institutes/grid.5254.6 schema:alternateName University of Copenhagen
153 schema:name Department of Mathematical Sciences, UCPH, Universitetsparken 5, 2100, Copenhague, Danemark
154 Université Pierre et Marie Curie, LSTA, 4 place Jussieu, 75005, Paris, France
155 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...