Nonparametric estimation of spatial distributions View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1983-06

AUTHORS

A. G. Journel

ABSTRACT

The indicator approach, whereby the data are used through their rank order, allows a nonparametric approach to the data bivariate distribution. Such rich structural information allows a nonparametric risk-qualified, estimation of local and global spatial distributions.

PAGES

445-468

References to SciGraph publications

Journal

TITLE

Mathematical Geosciences

ISSUE

3

VOLUME

15

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf01031292

DOI

http://dx.doi.org/10.1007/bf01031292

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038647337


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", 
    "author": [
      {
        "affiliation": {
          "alternateName": "Stanford University", 
          "id": "https://www.grid.ac/institutes/grid.168010.e", 
          "name": [
            "Applied Earth Sciences Department, Stanford University, 94305, Stanford, California, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Journel", 
        "givenName": "A. G.", 
        "id": "sg:person.013166227535.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013166227535.29"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-94-010-1470-0_14", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031348717", 
          "https://doi.org/10.1007/978-94-010-1470-0_14"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01036070", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045156199", 
          "https://doi.org/10.1007/bf01036070"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01036070", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045156199", 
          "https://doi.org/10.1007/bf01036070"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1983-06", 
    "datePublishedReg": "1983-06-01", 
    "description": "The indicator approach, whereby the data are used through their rank order, allows a nonparametric approach to the data bivariate distribution. Such rich structural information allows a nonparametric risk-qualified, estimation of local and global spatial distributions.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf01031292", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1039818", 
        "issn": [
          "1874-8961", 
          "1874-8953"
        ], 
        "name": "Mathematical Geosciences", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "15"
      }
    ], 
    "name": "Nonparametric estimation of spatial distributions", 
    "pagination": "445-468", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "b819209db0f2fb75176d8b5613a178efa66fb0d4bfdccf7ddc3a49800f5eaa69"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf01031292"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1038647337"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf01031292", 
      "https://app.dimensions.ai/details/publication/pub.1038647337"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:28", 
    "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/v90/0000000370_0000000370/records_46741_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF01031292"
  }
]
 

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/bf01031292'

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/bf01031292'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf01031292'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf01031292'


 

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

61 TRIPLES      20 PREDICATES      27 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf01031292 schema:author N146857a391184455bab774c95c432661
2 schema:citation sg:pub.10.1007/978-94-010-1470-0_14
3 sg:pub.10.1007/bf01036070
4 schema:datePublished 1983-06
5 schema:datePublishedReg 1983-06-01
6 schema:description The indicator approach, whereby the data are used through their rank order, allows a nonparametric approach to the data bivariate distribution. Such rich structural information allows a nonparametric risk-qualified, estimation of local and global spatial distributions.
7 schema:genre research_article
8 schema:inLanguage en
9 schema:isAccessibleForFree false
10 schema:isPartOf N2efeacbb396943deb69f0c04232c0ba0
11 Nb0579b754b1b48b4b7435d72af385f08
12 sg:journal.1039818
13 schema:name Nonparametric estimation of spatial distributions
14 schema:pagination 445-468
15 schema:productId N3cc09999b4ea4235b9a91197162c6f30
16 N9dc6f6e569d8488aa5aec2ca211eae07
17 Nd7e8972aae7340eebe6f8435977d715c
18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038647337
19 https://doi.org/10.1007/bf01031292
20 schema:sdDatePublished 2019-04-11T13:28
21 schema:sdLicense https://scigraph.springernature.com/explorer/license/
22 schema:sdPublisher N15f9a2c9a83141c1b849eb1ee5080a30
23 schema:url http://link.springer.com/10.1007/BF01031292
24 sgo:license sg:explorer/license/
25 sgo:sdDataset articles
26 rdf:type schema:ScholarlyArticle
27 N146857a391184455bab774c95c432661 rdf:first sg:person.013166227535.29
28 rdf:rest rdf:nil
29 N15f9a2c9a83141c1b849eb1ee5080a30 schema:name Springer Nature - SN SciGraph project
30 rdf:type schema:Organization
31 N2efeacbb396943deb69f0c04232c0ba0 schema:volumeNumber 15
32 rdf:type schema:PublicationVolume
33 N3cc09999b4ea4235b9a91197162c6f30 schema:name readcube_id
34 schema:value b819209db0f2fb75176d8b5613a178efa66fb0d4bfdccf7ddc3a49800f5eaa69
35 rdf:type schema:PropertyValue
36 N9dc6f6e569d8488aa5aec2ca211eae07 schema:name dimensions_id
37 schema:value pub.1038647337
38 rdf:type schema:PropertyValue
39 Nb0579b754b1b48b4b7435d72af385f08 schema:issueNumber 3
40 rdf:type schema:PublicationIssue
41 Nd7e8972aae7340eebe6f8435977d715c schema:name doi
42 schema:value 10.1007/bf01031292
43 rdf:type schema:PropertyValue
44 sg:journal.1039818 schema:issn 1874-8953
45 1874-8961
46 schema:name Mathematical Geosciences
47 rdf:type schema:Periodical
48 sg:person.013166227535.29 schema:affiliation https://www.grid.ac/institutes/grid.168010.e
49 schema:familyName Journel
50 schema:givenName A. G.
51 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013166227535.29
52 rdf:type schema:Person
53 sg:pub.10.1007/978-94-010-1470-0_14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031348717
54 https://doi.org/10.1007/978-94-010-1470-0_14
55 rdf:type schema:CreativeWork
56 sg:pub.10.1007/bf01036070 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045156199
57 https://doi.org/10.1007/bf01036070
58 rdf:type schema:CreativeWork
59 https://www.grid.ac/institutes/grid.168010.e schema:alternateName Stanford University
60 schema:name Applied Earth Sciences Department, Stanford University, 94305, Stanford, California, USA
61 rdf:type schema:Organization
 




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


...