Estimation of parameters in a two-parameter exponential distribution using ranked set sample View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1994-12

AUTHORS

Kin Lam, Bimal K. Sinha, Zhong Wu

ABSTRACT

In situations where the experimental or sampling units in a study can be easily ranked than quantified, McIntyre (1952,Aust. J. Agric. Res.,3, 385–390) proposed that the mean ofn units based on aranked set sample (RSS) be used to estimate the population mean, and observed that it provides an unbiased estimator with a smaller variance compared to a simple random sample (SRS) of the same sizen. McIntyre's concept ofRSS is essentially nonparametric in nature in that the underlying population distribution is assumed to be completely unknown. In this paper we further explore the concept ofRSS when the population is partially known and the parameter of interest is not necessarily the mean. To be specific, we address the problem of estimation of the parameters of a two-parameter exponential distribution. It turns out that the use ofRSS and its suitable modifications results in much improved estimators compared to the use of aSRS. More... »

PAGES

723-736

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00773478

DOI

http://dx.doi.org/10.1007/bf00773478

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1001374530


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/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Hong Kong", 
          "id": "https://www.grid.ac/institutes/grid.194645.b", 
          "name": [
            "Department of Statistics, University of Hong Kong, Pokfulam Road, Hong Kong"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lam", 
        "givenName": "Kin", 
        "id": "sg:person.011642761351.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011642761351.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Maryland, Baltimore County", 
          "id": "https://www.grid.ac/institutes/grid.266673.0", 
          "name": [
            "Department of Mathematics and Statistics, University of Maryland Baltimore County, 21228-5398, Baltimore, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sinha", 
        "givenName": "Bimal K.", 
        "id": "sg:person.012100000067.64", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012100000067.64"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Maryland, Baltimore County", 
          "id": "https://www.grid.ac/institutes/grid.266673.0", 
          "name": [
            "Department of Mathematics and Statistics, University of Maryland Baltimore County, 21228-5398, Baltimore, MD, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wu", 
        "givenName": "Zhong", 
        "id": "sg:person.011774242161.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011774242161.58"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf02506360", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001368770", 
          "https://doi.org/10.1007/bf02506360"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02506360", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001368770", 
          "https://doi.org/10.1007/bf02506360"
        ], 
        "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": "sg:pub.10.1007/bf02532252", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017297063", 
          "https://doi.org/10.1007/bf02532252"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02532252", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017297063", 
          "https://doi.org/10.1007/bf02532252"
        ], 
        "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.1365-2494.1985.tb01753.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026889965"
        ], 
        "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/10.1016/0378-3758(80)90031-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030178021"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1052490772", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4612-3644-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052490772", 
          "https://doi.org/10.1007/978-1-4612-3644-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4612-3644-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052490772", 
          "https://doi.org/10.1007/978-1-4612-3644-3"
        ], 
        "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/10.1214/aoms/1177706637", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064401105"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2530493", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069975998"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2531448", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069976887"
        ], 
        "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": "1994-12", 
    "datePublishedReg": "1994-12-01", 
    "description": "In situations where the experimental or sampling units in a study can be easily ranked than quantified, McIntyre (1952,Aust. J. Agric. Res.,3, 385\u2013390) proposed that the mean ofn units based on aranked set sample (RSS) be used to estimate the population mean, and observed that it provides an unbiased estimator with a smaller variance compared to a simple random sample (SRS) of the same sizen. McIntyre's concept ofRSS is essentially nonparametric in nature in that the underlying population distribution is assumed to be completely unknown. In this paper we further explore the concept ofRSS when the population is partially known and the parameter of interest is not necessarily the mean. To be specific, we address the problem of estimation of the parameters of a two-parameter exponential distribution. It turns out that the use ofRSS and its suitable modifications results in much improved estimators compared to the use of aSRS.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf00773478", 
    "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": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "46"
      }
    ], 
    "name": "Estimation of parameters in a two-parameter exponential distribution using ranked set sample", 
    "pagination": "723-736", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "068c0c4ff6ba9983e095ffe2d5e4a4ad9b57bc92cea2f85a139c4658dc2dd217"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf00773478"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1001374530"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf00773478", 
      "https://app.dimensions.ai/details/publication/pub.1001374530"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T00:59", 
    "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_8697_00000479.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF00773478"
  }
]
 

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/bf00773478'

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/bf00773478'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00773478'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00773478'


 

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

132 TRIPLES      21 PREDICATES      44 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf00773478 schema:about anzsrc-for:11
2 anzsrc-for:1117
3 schema:author Na2336f6980db45a985f28cce9d8c5057
4 schema:citation sg:pub.10.1007/978-1-4612-3644-3
5 sg:pub.10.1007/bf02506360
6 sg:pub.10.1007/bf02532252
7 sg:pub.10.1007/bf02911622
8 https://app.dimensions.ai/details/publication/pub.1052490772
9 https://doi.org/10.1016/0378-3758(80)90031-2
10 https://doi.org/10.1071/ar9520385
11 https://doi.org/10.1080/01621459.1988.10478607
12 https://doi.org/10.1080/03610927708827563
13 https://doi.org/10.1080/03610929008830198
14 https://doi.org/10.1111/j.1365-2494.1985.tb01753.x
15 https://doi.org/10.1111/j.1467-842x.1976.tb00963.x
16 https://doi.org/10.1214/aoms/1177706637
17 https://doi.org/10.2307/2347546
18 https://doi.org/10.2307/2530493
19 https://doi.org/10.2307/2531448
20 https://doi.org/10.2307/2556166
21 schema:datePublished 1994-12
22 schema:datePublishedReg 1994-12-01
23 schema:description In situations where the experimental or sampling units in a study can be easily ranked than quantified, McIntyre (1952,Aust. J. Agric. Res.,3, 385–390) proposed that the mean ofn units based on aranked set sample (RSS) be used to estimate the population mean, and observed that it provides an unbiased estimator with a smaller variance compared to a simple random sample (SRS) of the same sizen. McIntyre's concept ofRSS is essentially nonparametric in nature in that the underlying population distribution is assumed to be completely unknown. In this paper we further explore the concept ofRSS when the population is partially known and the parameter of interest is not necessarily the mean. To be specific, we address the problem of estimation of the parameters of a two-parameter exponential distribution. It turns out that the use ofRSS and its suitable modifications results in much improved estimators compared to the use of aSRS.
24 schema:genre research_article
25 schema:inLanguage en
26 schema:isAccessibleForFree false
27 schema:isPartOf N06239aa610994150aa95d6801fdaecc5
28 N59ff71f0ee194f158894667a39358aa1
29 sg:journal.1041657
30 schema:name Estimation of parameters in a two-parameter exponential distribution using ranked set sample
31 schema:pagination 723-736
32 schema:productId N0c38522b7133444881f782bfe5f5c339
33 N0f1a6af9a3ff4e6989ffe99ae0ffc99b
34 Nafa61f2152704587854cb55ea9b83647
35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001374530
36 https://doi.org/10.1007/bf00773478
37 schema:sdDatePublished 2019-04-11T00:59
38 schema:sdLicense https://scigraph.springernature.com/explorer/license/
39 schema:sdPublisher Nccae01fd86884852a4392bba42f392a8
40 schema:url http://link.springer.com/10.1007/BF00773478
41 sgo:license sg:explorer/license/
42 sgo:sdDataset articles
43 rdf:type schema:ScholarlyArticle
44 N061815820adc4e9d8f82ddb26197a6fd rdf:first sg:person.011774242161.58
45 rdf:rest rdf:nil
46 N06239aa610994150aa95d6801fdaecc5 schema:issueNumber 4
47 rdf:type schema:PublicationIssue
48 N0c38522b7133444881f782bfe5f5c339 schema:name doi
49 schema:value 10.1007/bf00773478
50 rdf:type schema:PropertyValue
51 N0f1a6af9a3ff4e6989ffe99ae0ffc99b schema:name readcube_id
52 schema:value 068c0c4ff6ba9983e095ffe2d5e4a4ad9b57bc92cea2f85a139c4658dc2dd217
53 rdf:type schema:PropertyValue
54 N351e6302cbfe4821b619a4df8bba9f7c rdf:first sg:person.012100000067.64
55 rdf:rest N061815820adc4e9d8f82ddb26197a6fd
56 N59ff71f0ee194f158894667a39358aa1 schema:volumeNumber 46
57 rdf:type schema:PublicationVolume
58 Na2336f6980db45a985f28cce9d8c5057 rdf:first sg:person.011642761351.00
59 rdf:rest N351e6302cbfe4821b619a4df8bba9f7c
60 Nafa61f2152704587854cb55ea9b83647 schema:name dimensions_id
61 schema:value pub.1001374530
62 rdf:type schema:PropertyValue
63 Nccae01fd86884852a4392bba42f392a8 schema:name Springer Nature - SN SciGraph project
64 rdf:type schema:Organization
65 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
66 schema:name Medical and Health Sciences
67 rdf:type schema:DefinedTerm
68 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
69 schema:name Public Health and Health Services
70 rdf:type schema:DefinedTerm
71 sg:journal.1041657 schema:issn 0020-3157
72 1572-9052
73 schema:name Annals of the Institute of Statistical Mathematics
74 rdf:type schema:Periodical
75 sg:person.011642761351.00 schema:affiliation https://www.grid.ac/institutes/grid.194645.b
76 schema:familyName Lam
77 schema:givenName Kin
78 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011642761351.00
79 rdf:type schema:Person
80 sg:person.011774242161.58 schema:affiliation https://www.grid.ac/institutes/grid.266673.0
81 schema:familyName Wu
82 schema:givenName Zhong
83 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011774242161.58
84 rdf:type schema:Person
85 sg:person.012100000067.64 schema:affiliation https://www.grid.ac/institutes/grid.266673.0
86 schema:familyName Sinha
87 schema:givenName Bimal K.
88 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012100000067.64
89 rdf:type schema:Person
90 sg:pub.10.1007/978-1-4612-3644-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052490772
91 https://doi.org/10.1007/978-1-4612-3644-3
92 rdf:type schema:CreativeWork
93 sg:pub.10.1007/bf02506360 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001368770
94 https://doi.org/10.1007/bf02506360
95 rdf:type schema:CreativeWork
96 sg:pub.10.1007/bf02532252 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017297063
97 https://doi.org/10.1007/bf02532252
98 rdf:type schema:CreativeWork
99 sg:pub.10.1007/bf02911622 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014193360
100 https://doi.org/10.1007/bf02911622
101 rdf:type schema:CreativeWork
102 https://app.dimensions.ai/details/publication/pub.1052490772 schema:CreativeWork
103 https://doi.org/10.1016/0378-3758(80)90031-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030178021
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1071/ar9520385 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024491920
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1080/01621459.1988.10478607 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058303584
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1080/03610927708827563 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058331964
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1080/03610929008830198 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058334668
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1111/j.1365-2494.1985.tb01753.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1026889965
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1111/j.1467-842x.1976.tb00963.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1027215700
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1214/aoms/1177706637 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064401105
118 rdf:type schema:CreativeWork
119 https://doi.org/10.2307/2347546 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101982998
120 rdf:type schema:CreativeWork
121 https://doi.org/10.2307/2530493 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069975998
122 rdf:type schema:CreativeWork
123 https://doi.org/10.2307/2531448 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069976887
124 rdf:type schema:CreativeWork
125 https://doi.org/10.2307/2556166 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069991898
126 rdf:type schema:CreativeWork
127 https://www.grid.ac/institutes/grid.194645.b schema:alternateName University of Hong Kong
128 schema:name Department of Statistics, University of Hong Kong, Pokfulam Road, Hong Kong
129 rdf:type schema:Organization
130 https://www.grid.ac/institutes/grid.266673.0 schema:alternateName University of Maryland, Baltimore County
131 schema:name Department of Mathematics and Statistics, University of Maryland Baltimore County, 21228-5398, Baltimore, MD, USA
132 rdf:type schema:Organization
 




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


...