Semiparametric multinomial logit models for analysing consumer choice behaviour View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2007-10

AUTHORS

Thomas Kneib, Bernhard Baumgartner, Winfried J. Steiner

ABSTRACT

The multinomial logit model (MNL) is one of the most frequently used statistical models in marketing applications. It allows one to relate an unordered categorical response variable, for example representing the choice of a brand, to a vector of covariates such as the price of the brand or variables characterising the consumer. In its classical form, all covariates enter in strictly parametric, linear form into the utility function of the MNL model. In this paper, we introduce semiparametric extensions, where smooth effects of continuous covariates are modelled by penalised splines. A mixed model representation of these penalised splines is employed to obtain estimates of the corresponding smoothing parameters, leading to a fully automated estimation procedure. To validate semiparametric models against parametric models, we utilise different scoring rules as well as predicted market share and compare parametric and semiparametric approaches for a number of brand choice data sets. More... »

PAGES

225-244

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10182-007-0033-2

DOI

http://dx.doi.org/10.1007/s10182-007-0033-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043719041


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/2008/1403", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Econometrics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/14", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Economics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Ludwig Maximilian University of Munich", 
          "id": "https://www.grid.ac/institutes/grid.5252.0", 
          "name": [
            "Department of Statistics, Ludwig-Maximilians-University, 80539, Munich, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kneib", 
        "givenName": "Thomas", 
        "id": "sg:person.01272020411.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01272020411.15"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Regensburg", 
          "id": "https://www.grid.ac/institutes/grid.7727.5", 
          "name": [
            "University of Regensburg, Universit\u00e4tsstrasse 31, 93053, Regensburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Baumgartner", 
        "givenName": "Bernhard", 
        "id": "sg:person.015545153613.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015545153613.01"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Regensburg", 
          "id": "https://www.grid.ac/institutes/grid.7727.5", 
          "name": [
            "University of Regensburg, Universit\u00e4tsstrasse 31, 93053, Regensburg, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Steiner", 
        "givenName": "Winfried J.", 
        "id": "sg:person.015543347315.47", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015543347315.47"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1111/j.1541-0420.2005.00392.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006302060"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1467-9868.00183", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007964683"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02753916", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011486548", 
          "https://doi.org/10.1007/bf02753916"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jspi.2003.09.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013065890"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.csda.2004.10.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015498897"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.csda.2005.12.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022324718"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0167-8116(99)00010-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026838335"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0022-4359(99)80107-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028123242"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/3-540-32539-5_95", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028133188", 
          "https://doi.org/10.1007/3-540-32539-5_95"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1030637857", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-3454-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030637857", 
          "https://doi.org/10.1007/978-1-4757-3454-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4757-3454-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030637857", 
          "https://doi.org/10.1007/978-1-4757-3454-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10182-006-0226-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033328494", 
          "https://doi.org/10.1007/s10182-006-0226-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10182-006-0226-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033328494", 
          "https://doi.org/10.1007/s10182-006-0226-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0167-4870(98)00006-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039657828"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/ss/1038425655", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041521657"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-9469.2006.00524.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050289937"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-9868.2006.00543.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053453333"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-9868.2006.00543.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053453333"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/209407", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058528994"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1086/296093", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058605919"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/016214506000001437", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064198608"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/106186008x287328", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064199616"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1287/mksc.7.1.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064713983"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1392286", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069469203"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1914185", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069641248"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3172584", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070216949"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3172645", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070217001"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/cbo9780511755453", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098667268"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/002224378802500202", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110432946"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/002224378802500202", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110432946"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/002224378802500202", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110432946"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/002224379002700301", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110841644"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/002224379002700301", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110841644"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/002224379002700301", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1110841644"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2007-10", 
    "datePublishedReg": "2007-10-01", 
    "description": "The multinomial logit model (MNL) is one of the most frequently used statistical models in marketing applications. It allows one to relate an unordered categorical response variable, for example representing the choice of a brand, to a vector of covariates such as the price of the brand or variables characterising the consumer. In its classical form, all covariates enter in strictly parametric, linear form into the utility function of the MNL model. In this paper, we introduce semiparametric extensions, where smooth effects of continuous covariates are modelled by penalised splines. A mixed model representation of these penalised splines is employed to obtain estimates of the corresponding smoothing parameters, leading to a fully automated estimation procedure. To validate semiparametric models against parametric models, we utilise different scoring rules as well as predicted market share and compare parametric and semiparametric approaches for a number of brand choice data sets.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10182-007-0033-2", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1312118", 
        "issn": [
          "1863-8171", 
          "1863-818X"
        ], 
        "name": "AStA Advances in Statistical Analysis", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "91"
      }
    ], 
    "name": "Semiparametric multinomial logit models for analysing consumer choice behaviour", 
    "pagination": "225-244", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "d04000d1a918997fae65398199fc9185cc8e11743b9be6ef1c61db207e791db6"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10182-007-0033-2"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1043719041"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10182-007-0033-2", 
      "https://app.dimensions.ai/details/publication/pub.1043719041"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T14:27", 
    "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/v90/0000000373_0000000373/records_13073_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s10182-007-0033-2"
  }
]
 

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/s10182-007-0033-2'

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/s10182-007-0033-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10182-007-0033-2'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10182-007-0033-2'


 

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

165 TRIPLES      21 PREDICATES      55 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10182-007-0033-2 schema:about anzsrc-for:14
2 anzsrc-for:1403
3 schema:author Ned02c355784c4c99ab81923075b86b0b
4 schema:citation sg:pub.10.1007/3-540-32539-5_95
5 sg:pub.10.1007/978-1-4757-3454-6
6 sg:pub.10.1007/bf02753916
7 sg:pub.10.1007/s10182-006-0226-0
8 https://app.dimensions.ai/details/publication/pub.1030637857
9 https://doi.org/10.1016/j.csda.2004.10.011
10 https://doi.org/10.1016/j.csda.2005.12.009
11 https://doi.org/10.1016/j.jspi.2003.09.023
12 https://doi.org/10.1016/s0022-4359(99)80107-9
13 https://doi.org/10.1016/s0167-4870(98)00006-3
14 https://doi.org/10.1016/s0167-8116(99)00010-5
15 https://doi.org/10.1017/cbo9780511755453
16 https://doi.org/10.1086/209407
17 https://doi.org/10.1086/296093
18 https://doi.org/10.1111/1467-9868.00183
19 https://doi.org/10.1111/j.1467-9469.2006.00524.x
20 https://doi.org/10.1111/j.1467-9868.2006.00543.x
21 https://doi.org/10.1111/j.1541-0420.2005.00392.x
22 https://doi.org/10.1177/002224378802500202
23 https://doi.org/10.1177/002224379002700301
24 https://doi.org/10.1198/016214506000001437
25 https://doi.org/10.1198/106186008x287328
26 https://doi.org/10.1214/ss/1038425655
27 https://doi.org/10.1287/mksc.7.1.1
28 https://doi.org/10.2307/1392286
29 https://doi.org/10.2307/1914185
30 https://doi.org/10.2307/3172584
31 https://doi.org/10.2307/3172645
32 schema:datePublished 2007-10
33 schema:datePublishedReg 2007-10-01
34 schema:description The multinomial logit model (MNL) is one of the most frequently used statistical models in marketing applications. It allows one to relate an unordered categorical response variable, for example representing the choice of a brand, to a vector of covariates such as the price of the brand or variables characterising the consumer. In its classical form, all covariates enter in strictly parametric, linear form into the utility function of the MNL model. In this paper, we introduce semiparametric extensions, where smooth effects of continuous covariates are modelled by penalised splines. A mixed model representation of these penalised splines is employed to obtain estimates of the corresponding smoothing parameters, leading to a fully automated estimation procedure. To validate semiparametric models against parametric models, we utilise different scoring rules as well as predicted market share and compare parametric and semiparametric approaches for a number of brand choice data sets.
35 schema:genre research_article
36 schema:inLanguage en
37 schema:isAccessibleForFree true
38 schema:isPartOf N31814c85675f407daaa2369706c3d5f3
39 N89f82286a25c4242a154616ad3143302
40 sg:journal.1312118
41 schema:name Semiparametric multinomial logit models for analysing consumer choice behaviour
42 schema:pagination 225-244
43 schema:productId Nb7372960967248e7b796ca4cbe2a7396
44 Nc98c6dcebfbd4079abe2e336d2afcb83
45 Nd314466b340d4474adffe51ee10681fb
46 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043719041
47 https://doi.org/10.1007/s10182-007-0033-2
48 schema:sdDatePublished 2019-04-11T14:27
49 schema:sdLicense https://scigraph.springernature.com/explorer/license/
50 schema:sdPublisher N225ec8fc676f4d939766f2bece465fba
51 schema:url http://link.springer.com/10.1007/s10182-007-0033-2
52 sgo:license sg:explorer/license/
53 sgo:sdDataset articles
54 rdf:type schema:ScholarlyArticle
55 N225ec8fc676f4d939766f2bece465fba schema:name Springer Nature - SN SciGraph project
56 rdf:type schema:Organization
57 N31814c85675f407daaa2369706c3d5f3 schema:issueNumber 3
58 rdf:type schema:PublicationIssue
59 N89f82286a25c4242a154616ad3143302 schema:volumeNumber 91
60 rdf:type schema:PublicationVolume
61 Na212a685ec3246069cde3fd25de1fdc7 rdf:first sg:person.015543347315.47
62 rdf:rest rdf:nil
63 Na84707c8a3e449c1a07c7b449cf0884b rdf:first sg:person.015545153613.01
64 rdf:rest Na212a685ec3246069cde3fd25de1fdc7
65 Nb7372960967248e7b796ca4cbe2a7396 schema:name readcube_id
66 schema:value d04000d1a918997fae65398199fc9185cc8e11743b9be6ef1c61db207e791db6
67 rdf:type schema:PropertyValue
68 Nc98c6dcebfbd4079abe2e336d2afcb83 schema:name doi
69 schema:value 10.1007/s10182-007-0033-2
70 rdf:type schema:PropertyValue
71 Nd314466b340d4474adffe51ee10681fb schema:name dimensions_id
72 schema:value pub.1043719041
73 rdf:type schema:PropertyValue
74 Ned02c355784c4c99ab81923075b86b0b rdf:first sg:person.01272020411.15
75 rdf:rest Na84707c8a3e449c1a07c7b449cf0884b
76 anzsrc-for:14 schema:inDefinedTermSet anzsrc-for:
77 schema:name Economics
78 rdf:type schema:DefinedTerm
79 anzsrc-for:1403 schema:inDefinedTermSet anzsrc-for:
80 schema:name Econometrics
81 rdf:type schema:DefinedTerm
82 sg:journal.1312118 schema:issn 1863-8171
83 1863-818X
84 schema:name AStA Advances in Statistical Analysis
85 rdf:type schema:Periodical
86 sg:person.01272020411.15 schema:affiliation https://www.grid.ac/institutes/grid.5252.0
87 schema:familyName Kneib
88 schema:givenName Thomas
89 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01272020411.15
90 rdf:type schema:Person
91 sg:person.015543347315.47 schema:affiliation https://www.grid.ac/institutes/grid.7727.5
92 schema:familyName Steiner
93 schema:givenName Winfried J.
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015543347315.47
95 rdf:type schema:Person
96 sg:person.015545153613.01 schema:affiliation https://www.grid.ac/institutes/grid.7727.5
97 schema:familyName Baumgartner
98 schema:givenName Bernhard
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015545153613.01
100 rdf:type schema:Person
101 sg:pub.10.1007/3-540-32539-5_95 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028133188
102 https://doi.org/10.1007/3-540-32539-5_95
103 rdf:type schema:CreativeWork
104 sg:pub.10.1007/978-1-4757-3454-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030637857
105 https://doi.org/10.1007/978-1-4757-3454-6
106 rdf:type schema:CreativeWork
107 sg:pub.10.1007/bf02753916 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011486548
108 https://doi.org/10.1007/bf02753916
109 rdf:type schema:CreativeWork
110 sg:pub.10.1007/s10182-006-0226-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033328494
111 https://doi.org/10.1007/s10182-006-0226-0
112 rdf:type schema:CreativeWork
113 https://app.dimensions.ai/details/publication/pub.1030637857 schema:CreativeWork
114 https://doi.org/10.1016/j.csda.2004.10.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015498897
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1016/j.csda.2005.12.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022324718
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1016/j.jspi.2003.09.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013065890
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1016/s0022-4359(99)80107-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028123242
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1016/s0167-4870(98)00006-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039657828
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1016/s0167-8116(99)00010-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026838335
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1017/cbo9780511755453 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098667268
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1086/209407 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058528994
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1086/296093 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058605919
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1111/1467-9868.00183 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007964683
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1111/j.1467-9469.2006.00524.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1050289937
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1111/j.1467-9868.2006.00543.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1053453333
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1111/j.1541-0420.2005.00392.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1006302060
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1177/002224378802500202 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110432946
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1177/002224379002700301 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110841644
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1198/016214506000001437 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064198608
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1198/106186008x287328 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064199616
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1214/ss/1038425655 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041521657
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1287/mksc.7.1.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064713983
151 rdf:type schema:CreativeWork
152 https://doi.org/10.2307/1392286 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069469203
153 rdf:type schema:CreativeWork
154 https://doi.org/10.2307/1914185 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069641248
155 rdf:type schema:CreativeWork
156 https://doi.org/10.2307/3172584 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070216949
157 rdf:type schema:CreativeWork
158 https://doi.org/10.2307/3172645 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070217001
159 rdf:type schema:CreativeWork
160 https://www.grid.ac/institutes/grid.5252.0 schema:alternateName Ludwig Maximilian University of Munich
161 schema:name Department of Statistics, Ludwig-Maximilians-University, 80539, Munich, Germany
162 rdf:type schema:Organization
163 https://www.grid.ac/institutes/grid.7727.5 schema:alternateName University of Regensburg
164 schema:name University of Regensburg, Universitätsstrasse 31, 93053, Regensburg, Germany
165 rdf:type schema:Organization
 




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


...