A dirichlet process elaboration diagnostic for binomial goodness of fit View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1998-06

AUTHORS

Cinzia Carota, Giovanni Parmigiani

ABSTRACT

Useful model checking tools can be constructed by measuring the distance between a prior distribution that concentrates most of its mass around a model of interest, and the resulting posterior distribution. In this paper we use this approach to construct a diagnostic measure for detecting lack of fit in discrete data, with special focus on binomial data. We begin by constructing a suitable probability model “around” the model of interest, via a Dirichlet Process elaboration. We derive the resulting diagnostic and show that, approximately, it is the sum of two terms: the first is the logarithm of the Bayes factor and the second is proportional to the Pearson chi-square statistics. We give details of a simulation algorithm for computing the diagnostic and illustrate its use in an application to biomedical data. More... »

PAGES

133-145

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf02565106

DOI

http://dx.doi.org/10.1007/bf02565106

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1020138072


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Pavia", 
          "id": "https://www.grid.ac/institutes/grid.8982.b", 
          "name": [
            "Istituto di Statistica, Facolt\u00e0 di Scienze Politiche, Universit\u00e0 di Pavia, Strada Nuova 65, 27100, Pavia, Italia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Carota", 
        "givenName": "Cinzia", 
        "id": "sg:person.012621717563.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012621717563.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Duke University", 
          "id": "https://www.grid.ac/institutes/grid.26009.3d", 
          "name": [
            "Institute of Statistics and Decision Sciences, Duke University, Box 90251, 27708-0251, Durham, NC, U.S.A."
          ], 
          "type": "Organization"
        }, 
        "familyName": "Parmigiani", 
        "givenName": "Giovanni", 
        "id": "sg:person.01213127733.91", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213127733.91"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1035713894", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4612-4578-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035713894", 
          "https://doi.org/10.1007/978-1-4612-4578-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4612-4578-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035713894", 
          "https://doi.org/10.1007/978-1-4612-4578-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aos/1176342871", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038214170"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aos/1176342360", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040652855"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00132613", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046857145", 
          "https://doi.org/10.1007/bf00132613"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00132613", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046857145", 
          "https://doi.org/10.1007/bf00132613"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1989.10478793", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058303770"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1991.10475146", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058304175"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1995.10476627", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058304910"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1996.10476943", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058305077"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aos/1034713652", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064406369"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1103194915", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1998-06", 
    "datePublishedReg": "1998-06-01", 
    "description": "Useful model checking tools can be constructed by measuring the distance between a prior distribution that concentrates most of its mass around a model of interest, and the resulting posterior distribution. In this paper we use this approach to construct a diagnostic measure for detecting lack of fit in discrete data, with special focus on binomial data. We begin by constructing a suitable probability model \u201caround\u201d the model of interest, via a Dirichlet Process elaboration. We derive the resulting diagnostic and show that, approximately, it is the sum of two terms: the first is the logarithm of the Bayes factor and the second is proportional to the Pearson chi-square statistics. We give details of a simulation algorithm for computing the diagnostic and illustrate its use in an application to biomedical data.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf02565106", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1043392", 
        "issn": [
          "1133-0686", 
          "1863-8260"
        ], 
        "name": "TEST", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "7"
      }
    ], 
    "name": "A dirichlet process elaboration diagnostic for binomial goodness of fit", 
    "pagination": "133-145", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "5ff3982d664847f6528c83b07ce6d5ed0a7dd71219c7b7fb18679e86b6d02cbf"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf02565106"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1020138072"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf02565106", 
      "https://app.dimensions.ai/details/publication/pub.1020138072"
    ], 
    "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_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2FBF02565106"
  }
]
 

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/bf02565106'

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/bf02565106'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf02565106'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf02565106'


 

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

104 TRIPLES      21 PREDICATES      38 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf02565106 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N994b3105346e48d08651b0ae0861ebd4
4 schema:citation sg:pub.10.1007/978-1-4612-4578-0
5 sg:pub.10.1007/bf00132613
6 https://app.dimensions.ai/details/publication/pub.1035713894
7 https://app.dimensions.ai/details/publication/pub.1103194915
8 https://doi.org/10.1080/01621459.1989.10478793
9 https://doi.org/10.1080/01621459.1991.10475146
10 https://doi.org/10.1080/01621459.1995.10476627
11 https://doi.org/10.1080/01621459.1996.10476943
12 https://doi.org/10.1214/aos/1034713652
13 https://doi.org/10.1214/aos/1176342360
14 https://doi.org/10.1214/aos/1176342871
15 schema:datePublished 1998-06
16 schema:datePublishedReg 1998-06-01
17 schema:description Useful model checking tools can be constructed by measuring the distance between a prior distribution that concentrates most of its mass around a model of interest, and the resulting posterior distribution. In this paper we use this approach to construct a diagnostic measure for detecting lack of fit in discrete data, with special focus on binomial data. We begin by constructing a suitable probability model “around” the model of interest, via a Dirichlet Process elaboration. We derive the resulting diagnostic and show that, approximately, it is the sum of two terms: the first is the logarithm of the Bayes factor and the second is proportional to the Pearson chi-square statistics. We give details of a simulation algorithm for computing the diagnostic and illustrate its use in an application to biomedical data.
18 schema:genre research_article
19 schema:inLanguage en
20 schema:isAccessibleForFree false
21 schema:isPartOf N5fb68be552d047d09e712352c8a1cc78
22 Naf913d26582a44c7a81a2c78e0ccd1c2
23 sg:journal.1043392
24 schema:name A dirichlet process elaboration diagnostic for binomial goodness of fit
25 schema:pagination 133-145
26 schema:productId N59a07ccd20014c40a04f6e0831a5da11
27 N5c47c9323d834748aad9840b7aa1344f
28 Nefadcb910aed46b2b18177c49e69275a
29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020138072
30 https://doi.org/10.1007/bf02565106
31 schema:sdDatePublished 2019-04-11T13:28
32 schema:sdLicense https://scigraph.springernature.com/explorer/license/
33 schema:sdPublisher N1d7b23e9388d4b82a6a45f20145c55fc
34 schema:url http://link.springer.com/10.1007%2FBF02565106
35 sgo:license sg:explorer/license/
36 sgo:sdDataset articles
37 rdf:type schema:ScholarlyArticle
38 N138703f1347f401d85ca968ebc3b3f15 rdf:first sg:person.01213127733.91
39 rdf:rest rdf:nil
40 N1d7b23e9388d4b82a6a45f20145c55fc schema:name Springer Nature - SN SciGraph project
41 rdf:type schema:Organization
42 N59a07ccd20014c40a04f6e0831a5da11 schema:name dimensions_id
43 schema:value pub.1020138072
44 rdf:type schema:PropertyValue
45 N5c47c9323d834748aad9840b7aa1344f schema:name doi
46 schema:value 10.1007/bf02565106
47 rdf:type schema:PropertyValue
48 N5fb68be552d047d09e712352c8a1cc78 schema:volumeNumber 7
49 rdf:type schema:PublicationVolume
50 N994b3105346e48d08651b0ae0861ebd4 rdf:first sg:person.012621717563.45
51 rdf:rest N138703f1347f401d85ca968ebc3b3f15
52 Naf913d26582a44c7a81a2c78e0ccd1c2 schema:issueNumber 1
53 rdf:type schema:PublicationIssue
54 Nefadcb910aed46b2b18177c49e69275a schema:name readcube_id
55 schema:value 5ff3982d664847f6528c83b07ce6d5ed0a7dd71219c7b7fb18679e86b6d02cbf
56 rdf:type schema:PropertyValue
57 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
58 schema:name Information and Computing Sciences
59 rdf:type schema:DefinedTerm
60 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
61 schema:name Artificial Intelligence and Image Processing
62 rdf:type schema:DefinedTerm
63 sg:journal.1043392 schema:issn 1133-0686
64 1863-8260
65 schema:name TEST
66 rdf:type schema:Periodical
67 sg:person.01213127733.91 schema:affiliation https://www.grid.ac/institutes/grid.26009.3d
68 schema:familyName Parmigiani
69 schema:givenName Giovanni
70 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01213127733.91
71 rdf:type schema:Person
72 sg:person.012621717563.45 schema:affiliation https://www.grid.ac/institutes/grid.8982.b
73 schema:familyName Carota
74 schema:givenName Cinzia
75 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012621717563.45
76 rdf:type schema:Person
77 sg:pub.10.1007/978-1-4612-4578-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035713894
78 https://doi.org/10.1007/978-1-4612-4578-0
79 rdf:type schema:CreativeWork
80 sg:pub.10.1007/bf00132613 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046857145
81 https://doi.org/10.1007/bf00132613
82 rdf:type schema:CreativeWork
83 https://app.dimensions.ai/details/publication/pub.1035713894 schema:CreativeWork
84 https://app.dimensions.ai/details/publication/pub.1103194915 schema:CreativeWork
85 https://doi.org/10.1080/01621459.1989.10478793 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058303770
86 rdf:type schema:CreativeWork
87 https://doi.org/10.1080/01621459.1991.10475146 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058304175
88 rdf:type schema:CreativeWork
89 https://doi.org/10.1080/01621459.1995.10476627 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058304910
90 rdf:type schema:CreativeWork
91 https://doi.org/10.1080/01621459.1996.10476943 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058305077
92 rdf:type schema:CreativeWork
93 https://doi.org/10.1214/aos/1034713652 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064406369
94 rdf:type schema:CreativeWork
95 https://doi.org/10.1214/aos/1176342360 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040652855
96 rdf:type schema:CreativeWork
97 https://doi.org/10.1214/aos/1176342871 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038214170
98 rdf:type schema:CreativeWork
99 https://www.grid.ac/institutes/grid.26009.3d schema:alternateName Duke University
100 schema:name Institute of Statistics and Decision Sciences, Duke University, Box 90251, 27708-0251, Durham, NC, U.S.A.
101 rdf:type schema:Organization
102 https://www.grid.ac/institutes/grid.8982.b schema:alternateName University of Pavia
103 schema:name Istituto di Statistica, Facoltà di Scienze Politiche, Università di Pavia, Strada Nuova 65, 27100, Pavia, Italia
104 rdf:type schema:Organization
 




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


...