Sampling from partially rank-ordered sets View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-12

AUTHORS

Omer Ozturk

ABSTRACT

In this paper we introduce a new sampling design. The proposed design is similar to a ranked set sampling (RSS) design with a clear difference that rankers are allowed to declare any two or more units are tied in ranks whenever the units can not be ranked with high confidence. These units are replaced in judgment subsets. The fully measured units are then selected from these partially ordered judgment subsets. Based on this sampling scheme, we develop unbiased estimators for the population mean and variance. We show that the proposed sampling procedure has some advantages over standard ranked set sampling. More... »

PAGES

757-779

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10651-010-0161-9

DOI

http://dx.doi.org/10.1007/s10651-010-0161-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1020025152


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/2008/0912", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Materials Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "The Ohio State University", 
          "id": "https://www.grid.ac/institutes/grid.261331.4", 
          "name": [
            "Department of Statistics, The Ohio State University, 404 Cockins Hall, 1958 Neil Ave., 43210, Columbus, OH, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ozturk", 
        "givenName": "Omer", 
        "id": "sg:person.01330772442.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01330772442.09"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s0378-3758(02)00497-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011267686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0378-3758(02)00497-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011267686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02911622", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014193360", 
          "https://doi.org/10.1007/bf02911622"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02911622", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014193360", 
          "https://doi.org/10.1007/bf02911622"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1023577356", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-387-21664-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023577356", 
          "https://doi.org/10.1007/978-0-387-21664-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-387-21664-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023577356", 
          "https://doi.org/10.1007/978-0-387-21664-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1071/ar9520385", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024491920"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jspi.2006.02.013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025085088"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10651-007-0023-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027816622", 
          "https://doi.org/10.1007/s10651-007-0023-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1467-9868.00331", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035190071"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1541-0420.2007.00900.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038685876"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/000313005x54180", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064197149"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/016214506000000410", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064198511"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/016214506000000564", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064198526"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2530493", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069975998"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2533102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069978507"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2556166", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069991898"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2011-12", 
    "datePublishedReg": "2011-12-01", 
    "description": "In this paper we introduce a new sampling design. The proposed design is similar to a ranked set sampling (RSS) design with a clear difference that rankers are allowed to declare any two or more units are tied in ranks whenever the units can not be ranked with high confidence. These units are replaced in judgment subsets. The fully measured units are then selected from these partially ordered judgment subsets. Based on this sampling scheme, we develop unbiased estimators for the population mean and variance. We show that the proposed sampling procedure has some advantages over standard ranked set sampling.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10651-010-0161-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1356907", 
        "issn": [
          "1573-3009", 
          "1352-8505"
        ], 
        "name": "Environmental and Ecological Statistics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "18"
      }
    ], 
    "name": "Sampling from partially rank-ordered sets", 
    "pagination": "757-779", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "5f6972fc1afae8c433f3708b62c937c4c77ab8db389f42ebf4f3eb989a3f1743"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10651-010-0161-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1020025152"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10651-010-0161-9", 
      "https://app.dimensions.ai/details/publication/pub.1020025152"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T22:31", 
    "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/v90/0000000001_0000000264/records_8690_00000512.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs10651-010-0161-9"
  }
]
 

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/s10651-010-0161-9'

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/s10651-010-0161-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10651-010-0161-9'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10651-010-0161-9'


 

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

108 TRIPLES      21 PREDICATES      42 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10651-010-0161-9 schema:about anzsrc-for:09
2 anzsrc-for:0912
3 schema:author Ncbde47c603d4457a9b1266fe5b4ebc05
4 schema:citation sg:pub.10.1007/978-0-387-21664-5
5 sg:pub.10.1007/bf02911622
6 sg:pub.10.1007/s10651-007-0023-2
7 https://app.dimensions.ai/details/publication/pub.1023577356
8 https://doi.org/10.1016/j.jspi.2006.02.013
9 https://doi.org/10.1016/s0378-3758(02)00497-4
10 https://doi.org/10.1071/ar9520385
11 https://doi.org/10.1111/1467-9868.00331
12 https://doi.org/10.1111/j.1541-0420.2007.00900.x
13 https://doi.org/10.1198/000313005x54180
14 https://doi.org/10.1198/016214506000000410
15 https://doi.org/10.1198/016214506000000564
16 https://doi.org/10.2307/2530493
17 https://doi.org/10.2307/2533102
18 https://doi.org/10.2307/2556166
19 schema:datePublished 2011-12
20 schema:datePublishedReg 2011-12-01
21 schema:description In this paper we introduce a new sampling design. The proposed design is similar to a ranked set sampling (RSS) design with a clear difference that rankers are allowed to declare any two or more units are tied in ranks whenever the units can not be ranked with high confidence. These units are replaced in judgment subsets. The fully measured units are then selected from these partially ordered judgment subsets. Based on this sampling scheme, we develop unbiased estimators for the population mean and variance. We show that the proposed sampling procedure has some advantages over standard ranked set sampling.
22 schema:genre research_article
23 schema:inLanguage en
24 schema:isAccessibleForFree true
25 schema:isPartOf N998f728af3de4e7cb026dae56aea912c
26 Ne54c5920c38d47188906775d37edcf1f
27 sg:journal.1356907
28 schema:name Sampling from partially rank-ordered sets
29 schema:pagination 757-779
30 schema:productId N14ed8418848446abaef40be398b111d3
31 N530566d1e1e5418fa0f7763b159d1c1f
32 Nf2f7f1cde1aa482d982b93587f5e34a6
33 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020025152
34 https://doi.org/10.1007/s10651-010-0161-9
35 schema:sdDatePublished 2019-04-10T22:31
36 schema:sdLicense https://scigraph.springernature.com/explorer/license/
37 schema:sdPublisher Ne7a256b1eaed4fd0a32a61c9af21dec4
38 schema:url http://link.springer.com/10.1007%2Fs10651-010-0161-9
39 sgo:license sg:explorer/license/
40 sgo:sdDataset articles
41 rdf:type schema:ScholarlyArticle
42 N14ed8418848446abaef40be398b111d3 schema:name doi
43 schema:value 10.1007/s10651-010-0161-9
44 rdf:type schema:PropertyValue
45 N530566d1e1e5418fa0f7763b159d1c1f schema:name readcube_id
46 schema:value 5f6972fc1afae8c433f3708b62c937c4c77ab8db389f42ebf4f3eb989a3f1743
47 rdf:type schema:PropertyValue
48 N998f728af3de4e7cb026dae56aea912c schema:volumeNumber 18
49 rdf:type schema:PublicationVolume
50 Ncbde47c603d4457a9b1266fe5b4ebc05 rdf:first sg:person.01330772442.09
51 rdf:rest rdf:nil
52 Ne54c5920c38d47188906775d37edcf1f schema:issueNumber 4
53 rdf:type schema:PublicationIssue
54 Ne7a256b1eaed4fd0a32a61c9af21dec4 schema:name Springer Nature - SN SciGraph project
55 rdf:type schema:Organization
56 Nf2f7f1cde1aa482d982b93587f5e34a6 schema:name dimensions_id
57 schema:value pub.1020025152
58 rdf:type schema:PropertyValue
59 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
60 schema:name Engineering
61 rdf:type schema:DefinedTerm
62 anzsrc-for:0912 schema:inDefinedTermSet anzsrc-for:
63 schema:name Materials Engineering
64 rdf:type schema:DefinedTerm
65 sg:journal.1356907 schema:issn 1352-8505
66 1573-3009
67 schema:name Environmental and Ecological Statistics
68 rdf:type schema:Periodical
69 sg:person.01330772442.09 schema:affiliation https://www.grid.ac/institutes/grid.261331.4
70 schema:familyName Ozturk
71 schema:givenName Omer
72 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01330772442.09
73 rdf:type schema:Person
74 sg:pub.10.1007/978-0-387-21664-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023577356
75 https://doi.org/10.1007/978-0-387-21664-5
76 rdf:type schema:CreativeWork
77 sg:pub.10.1007/bf02911622 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014193360
78 https://doi.org/10.1007/bf02911622
79 rdf:type schema:CreativeWork
80 sg:pub.10.1007/s10651-007-0023-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027816622
81 https://doi.org/10.1007/s10651-007-0023-2
82 rdf:type schema:CreativeWork
83 https://app.dimensions.ai/details/publication/pub.1023577356 schema:CreativeWork
84 https://doi.org/10.1016/j.jspi.2006.02.013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025085088
85 rdf:type schema:CreativeWork
86 https://doi.org/10.1016/s0378-3758(02)00497-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011267686
87 rdf:type schema:CreativeWork
88 https://doi.org/10.1071/ar9520385 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024491920
89 rdf:type schema:CreativeWork
90 https://doi.org/10.1111/1467-9868.00331 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035190071
91 rdf:type schema:CreativeWork
92 https://doi.org/10.1111/j.1541-0420.2007.00900.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1038685876
93 rdf:type schema:CreativeWork
94 https://doi.org/10.1198/000313005x54180 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064197149
95 rdf:type schema:CreativeWork
96 https://doi.org/10.1198/016214506000000410 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064198511
97 rdf:type schema:CreativeWork
98 https://doi.org/10.1198/016214506000000564 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064198526
99 rdf:type schema:CreativeWork
100 https://doi.org/10.2307/2530493 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069975998
101 rdf:type schema:CreativeWork
102 https://doi.org/10.2307/2533102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069978507
103 rdf:type schema:CreativeWork
104 https://doi.org/10.2307/2556166 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069991898
105 rdf:type schema:CreativeWork
106 https://www.grid.ac/institutes/grid.261331.4 schema:alternateName The Ohio State University
107 schema:name Department of Statistics, The Ohio State University, 404 Cockins Hall, 1958 Neil Ave., 43210, Columbus, OH, USA
108 rdf:type schema:Organization
 




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


...