Parametric ranked set sampling View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1995-09

AUTHORS

Lynne Stokes

ABSTRACT

Ranked set sampling was introduced by McIntyre (1952,Australian Journal of Agricultural Research,3, 385–390) as a cost-effective method of selecting data if observations are much more cheaply ranked than measured. He proposed its use for estimating the population mean when the distribution of the data was unknown. In this paper, we examine the advantage, if any, that this method of sampling has if the distribution is known, for a specific family of distributions. Specifically, we consider estimation of μ and σ for the family of random variables with cdf's of the formF(x−μ/σ). We find that the ranked set sample does provide more information about both μ and σ than a random sample of the same number of observations. We examine both maximum likelihood and best linear unbiased estimation of μ and σ, as well as methods for modifying the ranked set sampling procedure to provide even better estimation. More... »

PAGES

465-482

References to SciGraph publications

  • 1968-12. On unbiased estimates of the population mean based on the sample stratified by means of ordering in ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bf00773396

    DOI

    http://dx.doi.org/10.1007/bf00773396

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1086064729


    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": "The University of Texas at Austin", 
              "id": "https://www.grid.ac/institutes/grid.89336.37", 
              "name": [
                "Department of Management Science and Information Systems, University of Texas at Austin, CBA 5.202, 78712-1175, Austin, TX, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Stokes", 
            "givenName": "Lynne", 
            "id": "sg:person.016600157417.24", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016600157417.24"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1214/aoms/1177730881", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004554527"
            ], 
            "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://doi.org/10.1071/ar9520385", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024491920"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1467-842x.1976.tb00963.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027215700"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/env.3170040404", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044321043"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/01621459.1988.10478607", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058303584"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/03610927708827563", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058331964"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/03610929008830198", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1058334668"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2307/2530493", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1069975998"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2307/2556166", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1069991898"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2307/2347546", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101982998"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.2307/2347546", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1101982998"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "1995-09", 
        "datePublishedReg": "1995-09-01", 
        "description": "Ranked set sampling was introduced by McIntyre (1952,Australian Journal of Agricultural Research,3, 385\u2013390) as a cost-effective method of selecting data if observations are much more cheaply ranked than measured. He proposed its use for estimating the population mean when the distribution of the data was unknown. In this paper, we examine the advantage, if any, that this method of sampling has if the distribution is known, for a specific family of distributions. Specifically, we consider estimation of \u03bc and \u03c3 for the family of random variables with cdf's of the formF(x\u2212\u03bc/\u03c3). We find that the ranked set sample does provide more information about both \u03bc and \u03c3 than a random sample of the same number of observations. We examine both maximum likelihood and best linear unbiased estimation of \u03bc and \u03c3, as well as methods for modifying the ranked set sampling procedure to provide even better estimation.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/bf00773396", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1041657", 
            "issn": [
              "0020-3157", 
              "1572-9052"
            ], 
            "name": "Annals of the Institute of Statistical Mathematics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "3", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "47"
          }
        ], 
        "name": "Parametric ranked set sampling", 
        "pagination": "465-482", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "ab090e61c71809d11b32a89f41b5d7b144220660e46c252c6dbc0c444716e3d5"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/bf00773396"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1086064729"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/bf00773396", 
          "https://app.dimensions.ai/details/publication/pub.1086064729"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T14:54", 
        "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_8663_00000484.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007/BF00773396"
      }
    ]
     

    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/bf00773396'

    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/bf00773396'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00773396'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00773396'


     

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

    95 TRIPLES      21 PREDICATES      38 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/bf00773396 schema:about anzsrc-for:01
    2 anzsrc-for:0104
    3 schema:author N67e109c62c684b4a8044864d8e7191d5
    4 schema:citation sg:pub.10.1007/bf02911622
    5 https://doi.org/10.1002/env.3170040404
    6 https://doi.org/10.1071/ar9520385
    7 https://doi.org/10.1080/01621459.1988.10478607
    8 https://doi.org/10.1080/03610927708827563
    9 https://doi.org/10.1080/03610929008830198
    10 https://doi.org/10.1111/j.1467-842x.1976.tb00963.x
    11 https://doi.org/10.1214/aoms/1177730881
    12 https://doi.org/10.2307/2347546
    13 https://doi.org/10.2307/2530493
    14 https://doi.org/10.2307/2556166
    15 schema:datePublished 1995-09
    16 schema:datePublishedReg 1995-09-01
    17 schema:description Ranked set sampling was introduced by McIntyre (1952,Australian Journal of Agricultural Research,3, 385–390) as a cost-effective method of selecting data if observations are much more cheaply ranked than measured. He proposed its use for estimating the population mean when the distribution of the data was unknown. In this paper, we examine the advantage, if any, that this method of sampling has if the distribution is known, for a specific family of distributions. Specifically, we consider estimation of μ and σ for the family of random variables with cdf's of the formF(x−μ/σ). We find that the ranked set sample does provide more information about both μ and σ than a random sample of the same number of observations. We examine both maximum likelihood and best linear unbiased estimation of μ and σ, as well as methods for modifying the ranked set sampling procedure to provide even better estimation.
    18 schema:genre research_article
    19 schema:inLanguage en
    20 schema:isAccessibleForFree false
    21 schema:isPartOf N179cff1de81a4e6798198ac840c1b1c6
    22 N95beb78427fc465cb7c887a04d8b4303
    23 sg:journal.1041657
    24 schema:name Parametric ranked set sampling
    25 schema:pagination 465-482
    26 schema:productId N0dfbe23ba6954c18b9cd5d6a62d9f0c4
    27 N5a930a74d4704245989df9e943b86261
    28 N737d153fd38c4e37a1d8611bfc7a3f12
    29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086064729
    30 https://doi.org/10.1007/bf00773396
    31 schema:sdDatePublished 2019-04-10T14:54
    32 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    33 schema:sdPublisher Nf0b14c70a23642b7b64088960f62326d
    34 schema:url http://link.springer.com/10.1007/BF00773396
    35 sgo:license sg:explorer/license/
    36 sgo:sdDataset articles
    37 rdf:type schema:ScholarlyArticle
    38 N0dfbe23ba6954c18b9cd5d6a62d9f0c4 schema:name doi
    39 schema:value 10.1007/bf00773396
    40 rdf:type schema:PropertyValue
    41 N179cff1de81a4e6798198ac840c1b1c6 schema:issueNumber 3
    42 rdf:type schema:PublicationIssue
    43 N5a930a74d4704245989df9e943b86261 schema:name dimensions_id
    44 schema:value pub.1086064729
    45 rdf:type schema:PropertyValue
    46 N67e109c62c684b4a8044864d8e7191d5 rdf:first sg:person.016600157417.24
    47 rdf:rest rdf:nil
    48 N737d153fd38c4e37a1d8611bfc7a3f12 schema:name readcube_id
    49 schema:value ab090e61c71809d11b32a89f41b5d7b144220660e46c252c6dbc0c444716e3d5
    50 rdf:type schema:PropertyValue
    51 N95beb78427fc465cb7c887a04d8b4303 schema:volumeNumber 47
    52 rdf:type schema:PublicationVolume
    53 Nf0b14c70a23642b7b64088960f62326d schema:name Springer Nature - SN SciGraph project
    54 rdf:type schema:Organization
    55 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
    56 schema:name Mathematical Sciences
    57 rdf:type schema:DefinedTerm
    58 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
    59 schema:name Statistics
    60 rdf:type schema:DefinedTerm
    61 sg:journal.1041657 schema:issn 0020-3157
    62 1572-9052
    63 schema:name Annals of the Institute of Statistical Mathematics
    64 rdf:type schema:Periodical
    65 sg:person.016600157417.24 schema:affiliation https://www.grid.ac/institutes/grid.89336.37
    66 schema:familyName Stokes
    67 schema:givenName Lynne
    68 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016600157417.24
    69 rdf:type schema:Person
    70 sg:pub.10.1007/bf02911622 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014193360
    71 https://doi.org/10.1007/bf02911622
    72 rdf:type schema:CreativeWork
    73 https://doi.org/10.1002/env.3170040404 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044321043
    74 rdf:type schema:CreativeWork
    75 https://doi.org/10.1071/ar9520385 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024491920
    76 rdf:type schema:CreativeWork
    77 https://doi.org/10.1080/01621459.1988.10478607 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058303584
    78 rdf:type schema:CreativeWork
    79 https://doi.org/10.1080/03610927708827563 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058331964
    80 rdf:type schema:CreativeWork
    81 https://doi.org/10.1080/03610929008830198 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058334668
    82 rdf:type schema:CreativeWork
    83 https://doi.org/10.1111/j.1467-842x.1976.tb00963.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1027215700
    84 rdf:type schema:CreativeWork
    85 https://doi.org/10.1214/aoms/1177730881 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004554527
    86 rdf:type schema:CreativeWork
    87 https://doi.org/10.2307/2347546 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101982998
    88 rdf:type schema:CreativeWork
    89 https://doi.org/10.2307/2530493 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069975998
    90 rdf:type schema:CreativeWork
    91 https://doi.org/10.2307/2556166 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069991898
    92 rdf:type schema:CreativeWork
    93 https://www.grid.ac/institutes/grid.89336.37 schema:alternateName The University of Texas at Austin
    94 schema:name Department of Management Science and Information Systems, University of Texas at Austin, CBA 5.202, 78712-1175, Austin, TX, USA
    95 rdf:type schema:Organization
     




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


    ...