Generalized Semiparametric Regression with Covariates Measured with Error View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2009-12-29

AUTHORS

Thomas Kneib , Andreas Brezger , Ciprian M. Crainiceanu

ABSTRACT

We develop generalized semiparametric regression models for exponential family and hazard regression where multiple covariates are measured with error and the functional form of their effects remains unspecified. The main building blocks in our approach are Bayesian penalized splines and Markov chain Monte Carlo simulation techniques. These enable a modular and numerically efficient implementation of Bayesian measurement error correction based on the imputation of true, unobserved covariate values. We investigate the performance of the proposed correction in simulations and an epidemiological study where the duration time to detection of heart failure is related to kidney function and systolic blood pressure. More... »

PAGES

133-154

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-7908-2413-1_8

DOI

http://dx.doi.org/10.1007/978-3-7908-2413-1_8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1047644614


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": "Carl von Ossietzky University of Oldenburg", 
          "id": "https://www.grid.ac/institutes/grid.5560.6", 
          "name": [
            "Institut f\u00fcr Mathematik, Carl von Ossietzky Universit\u00e4t Oldenburg, 26111, Oldenburg, 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": {
          "name": [
            "HypoVereinsbank Munich, M\u00fcnchen, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Brezger", 
        "givenName": "Andreas", 
        "id": "sg:person.014355656153.78", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014355656153.78"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Johns Hopkins University", 
          "id": "https://www.grid.ac/institutes/grid.21107.35", 
          "name": [
            "Department of Biostatistics, Johns-Hopkins-University Baltimore, Baltimore, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Crainiceanu", 
        "givenName": "Ciprian M.", 
        "id": "sg:person.01131104325.42", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01131104325.42"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "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/10.1111/1467-9868.00288", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016269575"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.csda.2006.09.027", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018485270"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/sim.4780121806", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019428775"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1053/ajkd.2002.32765", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021983972"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1018509429360", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023622139", 
          "https://doi.org/10.1023/a:1018509429360"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1681/asn.2006101159", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026819683"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1681/asn.2006101159", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026819683"
        ], 
        "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": "https://doi.org/10.1001/jama.293.23.2892", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033910173"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1467-9868.00128", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041190779"
        ], 
        "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/10.1111/1467-9868.00247", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041544538"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1467-9876.00229", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045289617"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/1467-9876.00229", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045289617"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jspi.2008.05.036", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050076871"
        ], 
        "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/10.1093/oxfordjournals.aje.a115184", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059958072"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/016214502753479301", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064197979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/016214506000000348", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064198504"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/jasa.2009.tm08160", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064200471"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.18637/jss.v014.i11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068672215"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/1390616", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069468115"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1017/cbo9780511755453", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098667268"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1109390729", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1201/9781420010138", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109390729"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1201/9781420010404", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109616008"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2009-12-29", 
    "datePublishedReg": "2009-12-29", 
    "description": "We develop generalized semiparametric regression models for exponential family and hazard regression where multiple covariates are measured with error and the functional form of their effects remains unspecified. The main building blocks in our approach are Bayesian penalized splines and Markov chain Monte Carlo simulation techniques. These enable a modular and numerically efficient implementation of Bayesian measurement error correction based on the imputation of true, unobserved covariate values. We investigate the performance of the proposed correction in simulations and an epidemiological study where the duration time to detection of heart failure is related to kidney function and systolic blood pressure.", 
    "editor": [
      {
        "familyName": "Kneib", 
        "givenName": "Thomas", 
        "type": "Person"
      }, 
      {
        "familyName": "Tutz", 
        "givenName": "Gerhard", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-7908-2413-1_8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2430093", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2430092", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2430099", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2430096", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2430098", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2430097", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2430095", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2499161", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": {
      "isbn": [
        "978-3-7908-2412-4", 
        "978-3-7908-2413-1"
      ], 
      "name": "Statistical Modelling and Regression Structures", 
      "type": "Book"
    }, 
    "name": "Generalized Semiparametric Regression with Covariates Measured with Error", 
    "pagination": "133-154", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1047644614"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-7908-2413-1_8"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "e2f74f8ee7de0c459f12872903222ceed5446d18afdb8f3006b1abb0d7346ef8"
        ]
      }
    ], 
    "publisher": {
      "location": "Heidelberg", 
      "name": "Physica-Verlag HD", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-7908-2413-1_8", 
      "https://app.dimensions.ai/details/publication/pub.1047644614"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T07:29", 
    "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/v90/0000000356_0000000356/records_57864_00000000.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-3-7908-2413-1_8"
  }
]
 

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.10.1007/978-3-7908-2413-1_8'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-7908-2413-1_8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-7908-2413-1_8'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-7908-2413-1_8'


 

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

183 TRIPLES      23 PREDICATES      52 URIs      19 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-7908-2413-1_8 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author Nd99ea3d4e60944e4a0cf6c9c307b4984
4 schema:citation sg:pub.10.1007/978-1-4757-3454-6
5 sg:pub.10.1023/a:1018509429360
6 https://app.dimensions.ai/details/publication/pub.1030637857
7 https://app.dimensions.ai/details/publication/pub.1109390729
8 https://doi.org/10.1001/jama.293.23.2892
9 https://doi.org/10.1002/sim.4780121806
10 https://doi.org/10.1016/j.csda.2004.10.011
11 https://doi.org/10.1016/j.csda.2006.09.027
12 https://doi.org/10.1016/j.jspi.2008.05.036
13 https://doi.org/10.1017/cbo9780511755453
14 https://doi.org/10.1053/ajkd.2002.32765
15 https://doi.org/10.1093/oxfordjournals.aje.a115184
16 https://doi.org/10.1111/1467-9868.00128
17 https://doi.org/10.1111/1467-9868.00247
18 https://doi.org/10.1111/1467-9868.00288
19 https://doi.org/10.1111/1467-9876.00229
20 https://doi.org/10.1111/j.1467-9469.2006.00524.x
21 https://doi.org/10.1198/016214502753479301
22 https://doi.org/10.1198/016214506000000348
23 https://doi.org/10.1198/jasa.2009.tm08160
24 https://doi.org/10.1201/9781420010138
25 https://doi.org/10.1201/9781420010404
26 https://doi.org/10.1214/ss/1038425655
27 https://doi.org/10.1681/asn.2006101159
28 https://doi.org/10.18637/jss.v014.i11
29 https://doi.org/10.2307/1390616
30 schema:datePublished 2009-12-29
31 schema:datePublishedReg 2009-12-29
32 schema:description We develop generalized semiparametric regression models for exponential family and hazard regression where multiple covariates are measured with error and the functional form of their effects remains unspecified. The main building blocks in our approach are Bayesian penalized splines and Markov chain Monte Carlo simulation techniques. These enable a modular and numerically efficient implementation of Bayesian measurement error correction based on the imputation of true, unobserved covariate values. We investigate the performance of the proposed correction in simulations and an epidemiological study where the duration time to detection of heart failure is related to kidney function and systolic blood pressure.
33 schema:editor Nc1310f23c74e440298266b37a851e1de
34 schema:genre chapter
35 schema:inLanguage en
36 schema:isAccessibleForFree false
37 schema:isPartOf Nbf58384e0e5346b09937cb6384d9c830
38 schema:name Generalized Semiparametric Regression with Covariates Measured with Error
39 schema:pagination 133-154
40 schema:productId N344129b21c30466e8fd33be48b620096
41 Neb5ef2e997e646b2bf566d3527721324
42 Nf95872557df845408ed586853e48d849
43 schema:publisher N8ff578d4a55b417b98f10c02857e4f94
44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047644614
45 https://doi.org/10.1007/978-3-7908-2413-1_8
46 schema:sdDatePublished 2019-04-16T07:29
47 schema:sdLicense https://scigraph.springernature.com/explorer/license/
48 schema:sdPublisher N95d35116b40642a9a2bde51fefa9bdd6
49 schema:url https://link.springer.com/10.1007%2F978-3-7908-2413-1_8
50 sgo:license sg:explorer/license/
51 sgo:sdDataset chapters
52 rdf:type schema:Chapter
53 N00e1ddcd82814fbea89e2608861a325c schema:name HypoVereinsbank Munich, München, Germany
54 rdf:type schema:Organization
55 N344129b21c30466e8fd33be48b620096 schema:name readcube_id
56 schema:value e2f74f8ee7de0c459f12872903222ceed5446d18afdb8f3006b1abb0d7346ef8
57 rdf:type schema:PropertyValue
58 N54c0a8433adc408085af786d27a27e9c schema:familyName Tutz
59 schema:givenName Gerhard
60 rdf:type schema:Person
61 N593ce33867cc4fe5819791eeaa812d92 rdf:first sg:person.01131104325.42
62 rdf:rest rdf:nil
63 N71088648a6f4492e86146d73d2c786ca schema:familyName Kneib
64 schema:givenName Thomas
65 rdf:type schema:Person
66 N8442f43f13a142d3940ff279b9ab234f rdf:first sg:person.014355656153.78
67 rdf:rest N593ce33867cc4fe5819791eeaa812d92
68 N8ff578d4a55b417b98f10c02857e4f94 schema:location Heidelberg
69 schema:name Physica-Verlag HD
70 rdf:type schema:Organisation
71 N95d35116b40642a9a2bde51fefa9bdd6 schema:name Springer Nature - SN SciGraph project
72 rdf:type schema:Organization
73 Nbea4fa08565b4b8ca26b1ac804c52529 rdf:first N54c0a8433adc408085af786d27a27e9c
74 rdf:rest rdf:nil
75 Nbf58384e0e5346b09937cb6384d9c830 schema:isbn 978-3-7908-2412-4
76 978-3-7908-2413-1
77 schema:name Statistical Modelling and Regression Structures
78 rdf:type schema:Book
79 Nc1310f23c74e440298266b37a851e1de rdf:first N71088648a6f4492e86146d73d2c786ca
80 rdf:rest Nbea4fa08565b4b8ca26b1ac804c52529
81 Nd99ea3d4e60944e4a0cf6c9c307b4984 rdf:first sg:person.01272020411.15
82 rdf:rest N8442f43f13a142d3940ff279b9ab234f
83 Neb5ef2e997e646b2bf566d3527721324 schema:name doi
84 schema:value 10.1007/978-3-7908-2413-1_8
85 rdf:type schema:PropertyValue
86 Nf95872557df845408ed586853e48d849 schema:name dimensions_id
87 schema:value pub.1047644614
88 rdf:type schema:PropertyValue
89 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
90 schema:name Mathematical Sciences
91 rdf:type schema:DefinedTerm
92 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
93 schema:name Statistics
94 rdf:type schema:DefinedTerm
95 sg:grant.2430092 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-7908-2413-1_8
96 rdf:type schema:MonetaryGrant
97 sg:grant.2430093 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-7908-2413-1_8
98 rdf:type schema:MonetaryGrant
99 sg:grant.2430095 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-7908-2413-1_8
100 rdf:type schema:MonetaryGrant
101 sg:grant.2430096 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-7908-2413-1_8
102 rdf:type schema:MonetaryGrant
103 sg:grant.2430097 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-7908-2413-1_8
104 rdf:type schema:MonetaryGrant
105 sg:grant.2430098 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-7908-2413-1_8
106 rdf:type schema:MonetaryGrant
107 sg:grant.2430099 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-7908-2413-1_8
108 rdf:type schema:MonetaryGrant
109 sg:grant.2499161 http://pending.schema.org/fundedItem sg:pub.10.1007/978-3-7908-2413-1_8
110 rdf:type schema:MonetaryGrant
111 sg:person.01131104325.42 schema:affiliation https://www.grid.ac/institutes/grid.21107.35
112 schema:familyName Crainiceanu
113 schema:givenName Ciprian M.
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01131104325.42
115 rdf:type schema:Person
116 sg:person.01272020411.15 schema:affiliation https://www.grid.ac/institutes/grid.5560.6
117 schema:familyName Kneib
118 schema:givenName Thomas
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01272020411.15
120 rdf:type schema:Person
121 sg:person.014355656153.78 schema:affiliation N00e1ddcd82814fbea89e2608861a325c
122 schema:familyName Brezger
123 schema:givenName Andreas
124 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014355656153.78
125 rdf:type schema:Person
126 sg:pub.10.1007/978-1-4757-3454-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030637857
127 https://doi.org/10.1007/978-1-4757-3454-6
128 rdf:type schema:CreativeWork
129 sg:pub.10.1023/a:1018509429360 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023622139
130 https://doi.org/10.1023/a:1018509429360
131 rdf:type schema:CreativeWork
132 https://app.dimensions.ai/details/publication/pub.1030637857 schema:CreativeWork
133 https://app.dimensions.ai/details/publication/pub.1109390729 schema:CreativeWork
134 https://doi.org/10.1001/jama.293.23.2892 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033910173
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1002/sim.4780121806 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019428775
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1016/j.csda.2004.10.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015498897
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1016/j.csda.2006.09.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018485270
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1016/j.jspi.2008.05.036 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050076871
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1017/cbo9780511755453 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098667268
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1053/ajkd.2002.32765 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021983972
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1093/oxfordjournals.aje.a115184 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059958072
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1111/1467-9868.00128 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041190779
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1111/1467-9868.00247 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041544538
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1111/1467-9868.00288 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016269575
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1111/1467-9876.00229 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045289617
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1111/j.1467-9469.2006.00524.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1050289937
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1198/016214502753479301 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064197979
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1198/016214506000000348 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064198504
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1198/jasa.2009.tm08160 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064200471
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1201/9781420010138 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109390729
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1201/9781420010404 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109616008
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1214/ss/1038425655 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041521657
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1681/asn.2006101159 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026819683
173 rdf:type schema:CreativeWork
174 https://doi.org/10.18637/jss.v014.i11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068672215
175 rdf:type schema:CreativeWork
176 https://doi.org/10.2307/1390616 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069468115
177 rdf:type schema:CreativeWork
178 https://www.grid.ac/institutes/grid.21107.35 schema:alternateName Johns Hopkins University
179 schema:name Department of Biostatistics, Johns-Hopkins-University Baltimore, Baltimore, USA
180 rdf:type schema:Organization
181 https://www.grid.ac/institutes/grid.5560.6 schema:alternateName Carl von Ossietzky University of Oldenburg
182 schema:name Institut für Mathematik, Carl von Ossietzky Universität Oldenburg, 26111, Oldenburg, Germany
183 rdf:type schema:Organization
 




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


...