A unified framework of constrained regression View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-01

AUTHORS

Benjamin Hofner, Thomas Kneib, Torsten Hothorn

ABSTRACT

Generalized additive models (GAMs) play an important role in modeling and understanding complex relationships in modern applied statistics. They allow for flexible, data-driven estimation of covariate effects. Yet researchers often have a priori knowledge of certain effects, which might be monotonic or periodic (cyclic) or should fulfill boundary conditions. We propose a unified framework to incorporate these constraints for both univariate and bivariate effect estimates and for varying coefficients. As the framework is based on component-wise boosting methods, variables can be selected intrinsically, and effects can be estimated for a wide range of different distributional assumptions. Bootstrap confidence intervals for the effect estimates are derived to assess the models. We present three case studies from environmental sciences to illustrate the proposed seamless modeling framework. All discussed constrained effect estimates are implemented in the comprehensive R package mboost for model-based boosting. More... »

PAGES

1-14

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11222-014-9520-y

DOI

http://dx.doi.org/10.1007/s11222-014-9520-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1045070158


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": "University of Erlangen-Nuremberg", 
          "id": "https://www.grid.ac/institutes/grid.5330.5", 
          "name": [
            "Institut f\u00fcr Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Waldstra\u00dfe 6, 91054, Erlangen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hofner", 
        "givenName": "Benjamin", 
        "id": "sg:person.01044432226.13", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01044432226.13"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of G\u00f6ttingen", 
          "id": "https://www.grid.ac/institutes/grid.7450.6", 
          "name": [
            "Lehrstuhl f\u00fcr Statistik, Georg-August-Universit\u00e4t G\u00f6ttingen, Platz der G\u00f6ttinger Sieben 5, 37073, G\u00f6ttingen, 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 Zurich", 
          "id": "https://www.grid.ac/institutes/grid.7400.3", 
          "name": [
            "Institut f\u00fcr Epidemiologie, Biostatistik und Pr\u00e4vention, Abteilung Biostatistik, Universit\u00e4t Z\u00fcrich, Hirschengraben 84, 8001, Z\u00fcrich, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hothorn", 
        "givenName": "Torsten", 
        "id": "sg:person.0637301571.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0637301571.01"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1111/j.1467-9868.2010.00740.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000696823"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-9868.2010.00740.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000696823"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/10-2276.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000901718"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00180-012-0382-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001684018", 
          "https://doi.org/10.1007/s00180-012-0382-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/rssb.12017", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003716532"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-9868.2011.01034.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012681255"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1541-0420.2008.01112.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018081846"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bfb0092976", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018634553", 
          "https://doi.org/10.1007/bfb0092976"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.csda.2010.11.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021359513"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00180-012-0337-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022087125", 
          "https://doi.org/10.1007/s00180-012-0337-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12859-015-0575-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028801735", 
          "https://doi.org/10.1186/s12859-015-0575-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12859-015-0575-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028801735", 
          "https://doi.org/10.1186/s12859-015-0575-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1541-0420.2006.00574.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028992075"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02591962", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034228875", 
          "https://doi.org/10.1007/bf02591962"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02591962", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034228875", 
          "https://doi.org/10.1007/bf02591962"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0061623", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034566610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/wics.125", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039873591"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/wics.125", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039873591"
        ], 
        "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/10.1348/000711005x84293", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041716947"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/cem.935", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042200635"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/cem.935", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042200635"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-9868.2007.00646.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042539123"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-9876.2005.00510.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043199556"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/00039896.1995.9940893", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044714432"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1371/journal.pone.0029510", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045139686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11222-013-9448-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047330060", 
          "https://doi.org/10.1007/s11222-013-9448-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11222-013-9448-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047330060", 
          "https://doi.org/10.1007/s11222-013-9448-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.csda.2008.09.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047435658"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/07-sts242", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049744920"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1467-9876.2011.01033.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051869147"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/016214503000125", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064198102"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/jasa.2010.tm09165", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064200594"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/jasa.2011.ap09272", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064200650"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/jcgs.2011.09220", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064201123"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/jcgs.2011.10095", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064201128"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.18637/jss.v032.i05", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068672483"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.18637/jss.v074.i01", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068673116"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/3434781", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070323913"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3150/bj/1151525131", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071057324"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.25225/fozo.v62.i1.a10.2013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100176139"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-01", 
    "datePublishedReg": "2016-01-01", 
    "description": "Generalized additive models (GAMs) play an important role in modeling and understanding complex relationships in modern applied statistics. They allow for flexible, data-driven estimation of covariate effects. Yet researchers often have a priori knowledge of certain effects, which might be monotonic or periodic (cyclic) or should fulfill boundary conditions. We propose a unified framework to incorporate these constraints for both univariate and bivariate effect estimates and for varying coefficients. As the framework is based on component-wise boosting methods, variables can be selected intrinsically, and effects can be estimated for a wide range of different distributional assumptions. Bootstrap confidence intervals for the effect estimates are derived to assess the models. We present three case studies from environmental sciences to illustrate the proposed seamless modeling framework. All discussed constrained effect estimates are implemented in the comprehensive R package mboost for model-based boosting.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11222-014-9520-y", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1327447", 
        "issn": [
          "0960-3174", 
          "1573-1375"
        ], 
        "name": "Statistics and Computing", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1-2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "26"
      }
    ], 
    "name": "A unified framework of constrained regression", 
    "pagination": "1-14", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "d6aef9247555a73b885fc921aa5eed0b8f9445afb9b1036b573b589f49c98e4e"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11222-014-9520-y"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1045070158"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11222-014-9520-y", 
      "https://app.dimensions.ai/details/publication/pub.1045070158"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T02:11", 
    "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_8700_00000524.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs11222-014-9520-y"
  }
]
 

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/s11222-014-9520-y'

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/s11222-014-9520-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11222-014-9520-y'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11222-014-9520-y'


 

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

192 TRIPLES      21 PREDICATES      62 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11222-014-9520-y schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author N80e2014e1a1b49d197d6d563afe9d479
4 schema:citation sg:pub.10.1007/bf02591962
5 sg:pub.10.1007/bfb0092976
6 sg:pub.10.1007/s00180-012-0337-x
7 sg:pub.10.1007/s00180-012-0382-5
8 sg:pub.10.1007/s11222-013-9448-7
9 sg:pub.10.1186/s12859-015-0575-3
10 https://doi.org/10.1002/cem.935
11 https://doi.org/10.1002/wics.125
12 https://doi.org/10.1016/j.csda.2008.09.009
13 https://doi.org/10.1016/j.csda.2010.11.015
14 https://doi.org/10.1080/00039896.1995.9940893
15 https://doi.org/10.1111/j.1467-9868.2007.00646.x
16 https://doi.org/10.1111/j.1467-9868.2010.00740.x
17 https://doi.org/10.1111/j.1467-9868.2011.01034.x
18 https://doi.org/10.1111/j.1467-9876.2005.00510.x
19 https://doi.org/10.1111/j.1467-9876.2011.01033.x
20 https://doi.org/10.1111/j.1541-0420.2006.00574.x
21 https://doi.org/10.1111/j.1541-0420.2008.01112.x
22 https://doi.org/10.1111/rssb.12017
23 https://doi.org/10.1198/016214503000125
24 https://doi.org/10.1198/jasa.2010.tm09165
25 https://doi.org/10.1198/jasa.2011.ap09272
26 https://doi.org/10.1198/jcgs.2011.09220
27 https://doi.org/10.1198/jcgs.2011.10095
28 https://doi.org/10.1214/07-sts242
29 https://doi.org/10.1214/ss/1038425655
30 https://doi.org/10.1348/000711005x84293
31 https://doi.org/10.1371/journal.pone.0029510
32 https://doi.org/10.1371/journal.pone.0061623
33 https://doi.org/10.18637/jss.v032.i05
34 https://doi.org/10.18637/jss.v074.i01
35 https://doi.org/10.1890/10-2276.1
36 https://doi.org/10.2307/3434781
37 https://doi.org/10.25225/fozo.v62.i1.a10.2013
38 https://doi.org/10.3150/bj/1151525131
39 schema:datePublished 2016-01
40 schema:datePublishedReg 2016-01-01
41 schema:description Generalized additive models (GAMs) play an important role in modeling and understanding complex relationships in modern applied statistics. They allow for flexible, data-driven estimation of covariate effects. Yet researchers often have a priori knowledge of certain effects, which might be monotonic or periodic (cyclic) or should fulfill boundary conditions. We propose a unified framework to incorporate these constraints for both univariate and bivariate effect estimates and for varying coefficients. As the framework is based on component-wise boosting methods, variables can be selected intrinsically, and effects can be estimated for a wide range of different distributional assumptions. Bootstrap confidence intervals for the effect estimates are derived to assess the models. We present three case studies from environmental sciences to illustrate the proposed seamless modeling framework. All discussed constrained effect estimates are implemented in the comprehensive R package mboost for model-based boosting.
42 schema:genre research_article
43 schema:inLanguage en
44 schema:isAccessibleForFree true
45 schema:isPartOf N4d1975effc3146c5ac45755f270f04f1
46 N5ec0976ad76b413696ed27ca29a51c87
47 sg:journal.1327447
48 schema:name A unified framework of constrained regression
49 schema:pagination 1-14
50 schema:productId N80a7c1a16cae454680bce64ff654872b
51 Nb3b5a3f4789242f79b3fc738d80aa043
52 Nd73a1c288b0d4b32bbe6454024b2212d
53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045070158
54 https://doi.org/10.1007/s11222-014-9520-y
55 schema:sdDatePublished 2019-04-11T02:11
56 schema:sdLicense https://scigraph.springernature.com/explorer/license/
57 schema:sdPublisher Naba58cf530ad486287b4dd5ddb7aa7d4
58 schema:url http://link.springer.com/10.1007%2Fs11222-014-9520-y
59 sgo:license sg:explorer/license/
60 sgo:sdDataset articles
61 rdf:type schema:ScholarlyArticle
62 N4d1975effc3146c5ac45755f270f04f1 schema:volumeNumber 26
63 rdf:type schema:PublicationVolume
64 N5ec0976ad76b413696ed27ca29a51c87 schema:issueNumber 1-2
65 rdf:type schema:PublicationIssue
66 N6301d03120054c94acf77186c8bb0ae2 rdf:first sg:person.0637301571.01
67 rdf:rest rdf:nil
68 N80a7c1a16cae454680bce64ff654872b schema:name doi
69 schema:value 10.1007/s11222-014-9520-y
70 rdf:type schema:PropertyValue
71 N80e2014e1a1b49d197d6d563afe9d479 rdf:first sg:person.01044432226.13
72 rdf:rest Ne6d74f1a857e4e51b0a4b45fbcfcd755
73 Naba58cf530ad486287b4dd5ddb7aa7d4 schema:name Springer Nature - SN SciGraph project
74 rdf:type schema:Organization
75 Nb3b5a3f4789242f79b3fc738d80aa043 schema:name readcube_id
76 schema:value d6aef9247555a73b885fc921aa5eed0b8f9445afb9b1036b573b589f49c98e4e
77 rdf:type schema:PropertyValue
78 Nd73a1c288b0d4b32bbe6454024b2212d schema:name dimensions_id
79 schema:value pub.1045070158
80 rdf:type schema:PropertyValue
81 Ne6d74f1a857e4e51b0a4b45fbcfcd755 rdf:first sg:person.01272020411.15
82 rdf:rest N6301d03120054c94acf77186c8bb0ae2
83 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
84 schema:name Mathematical Sciences
85 rdf:type schema:DefinedTerm
86 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
87 schema:name Statistics
88 rdf:type schema:DefinedTerm
89 sg:journal.1327447 schema:issn 0960-3174
90 1573-1375
91 schema:name Statistics and Computing
92 rdf:type schema:Periodical
93 sg:person.01044432226.13 schema:affiliation https://www.grid.ac/institutes/grid.5330.5
94 schema:familyName Hofner
95 schema:givenName Benjamin
96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01044432226.13
97 rdf:type schema:Person
98 sg:person.01272020411.15 schema:affiliation https://www.grid.ac/institutes/grid.7450.6
99 schema:familyName Kneib
100 schema:givenName Thomas
101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01272020411.15
102 rdf:type schema:Person
103 sg:person.0637301571.01 schema:affiliation https://www.grid.ac/institutes/grid.7400.3
104 schema:familyName Hothorn
105 schema:givenName Torsten
106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0637301571.01
107 rdf:type schema:Person
108 sg:pub.10.1007/bf02591962 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034228875
109 https://doi.org/10.1007/bf02591962
110 rdf:type schema:CreativeWork
111 sg:pub.10.1007/bfb0092976 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018634553
112 https://doi.org/10.1007/bfb0092976
113 rdf:type schema:CreativeWork
114 sg:pub.10.1007/s00180-012-0337-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1022087125
115 https://doi.org/10.1007/s00180-012-0337-x
116 rdf:type schema:CreativeWork
117 sg:pub.10.1007/s00180-012-0382-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001684018
118 https://doi.org/10.1007/s00180-012-0382-5
119 rdf:type schema:CreativeWork
120 sg:pub.10.1007/s11222-013-9448-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047330060
121 https://doi.org/10.1007/s11222-013-9448-7
122 rdf:type schema:CreativeWork
123 sg:pub.10.1186/s12859-015-0575-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028801735
124 https://doi.org/10.1186/s12859-015-0575-3
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1002/cem.935 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042200635
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1002/wics.125 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039873591
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1016/j.csda.2008.09.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047435658
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1016/j.csda.2010.11.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021359513
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1080/00039896.1995.9940893 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044714432
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1111/j.1467-9868.2007.00646.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1042539123
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1111/j.1467-9868.2010.00740.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1000696823
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1111/j.1467-9868.2011.01034.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1012681255
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1111/j.1467-9876.2005.00510.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1043199556
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1111/j.1467-9876.2011.01033.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1051869147
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1111/j.1541-0420.2006.00574.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1028992075
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1111/j.1541-0420.2008.01112.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1018081846
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1111/rssb.12017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003716532
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1198/016214503000125 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064198102
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1198/jasa.2010.tm09165 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064200594
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1198/jasa.2011.ap09272 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064200650
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1198/jcgs.2011.09220 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064201123
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1198/jcgs.2011.10095 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064201128
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1214/07-sts242 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049744920
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1214/ss/1038425655 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041521657
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1348/000711005x84293 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041716947
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1371/journal.pone.0029510 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045139686
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1371/journal.pone.0061623 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034566610
171 rdf:type schema:CreativeWork
172 https://doi.org/10.18637/jss.v032.i05 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068672483
173 rdf:type schema:CreativeWork
174 https://doi.org/10.18637/jss.v074.i01 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068673116
175 rdf:type schema:CreativeWork
176 https://doi.org/10.1890/10-2276.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000901718
177 rdf:type schema:CreativeWork
178 https://doi.org/10.2307/3434781 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070323913
179 rdf:type schema:CreativeWork
180 https://doi.org/10.25225/fozo.v62.i1.a10.2013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100176139
181 rdf:type schema:CreativeWork
182 https://doi.org/10.3150/bj/1151525131 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071057324
183 rdf:type schema:CreativeWork
184 https://www.grid.ac/institutes/grid.5330.5 schema:alternateName University of Erlangen-Nuremberg
185 schema:name Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Waldstraße 6, 91054, Erlangen, Germany
186 rdf:type schema:Organization
187 https://www.grid.ac/institutes/grid.7400.3 schema:alternateName University of Zurich
188 schema:name Institut für Epidemiologie, Biostatistik und Prävention, Abteilung Biostatistik, Universität Zürich, Hirschengraben 84, 8001, Zürich, Switzerland
189 rdf:type schema:Organization
190 https://www.grid.ac/institutes/grid.7450.6 schema:alternateName University of Göttingen
191 schema:name Lehrstuhl für Statistik, Georg-August-Universität Göttingen, Platz der Göttinger Sieben 5, 37073, Göttingen, Germany
192 rdf:type schema:Organization
 




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


...