Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-05

AUTHORS

Janek Thomas, Andreas Mayr, Bernd Bischl, Matthias Schmid, Adam Smith, Benjamin Hofner

ABSTRACT

We present a new algorithm for boosting generalized additive models for location, scale and shape (GAMLSS) that allows to incorporate stability selection, an increasingly popular way to obtain stable sets of covariates while controlling the per-family error rate. The model is fitted repeatedly to subsampled data, and variables with high selection frequencies are extracted. To apply stability selection to boosted GAMLSS, we develop a new “noncyclical” fitting algorithm that incorporates an additional selection step of the best-fitting distribution parameter in each iteration. This new algorithm has the additional advantage that optimizing the tuning parameters of boosting is reduced from a multi-dimensional to a one-dimensional problem with vastly decreased complexity. The performance of the novel algorithm is evaluated in an extensive simulation study. We apply this new algorithm to a study to estimate abundance of common eider in Massachusetts, USA, featuring excess zeros, overdispersion, nonlinearity and spatiotemporal structures. Eider abundance is estimated via boosted GAMLSS, allowing both mean and overdispersion to be regressed on covariates. Stability selection is used to obtain a sparse set of stable predictors. More... »

PAGES

673-687

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11222-017-9754-6

DOI

http://dx.doi.org/10.1007/s11222-017-9754-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085396828


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/2008/0103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Numerical and Computational Mathematics", 
        "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": "Ludwig Maximilian University of Munich", 
          "id": "https://www.grid.ac/institutes/grid.5252.0", 
          "name": [
            "Department of Statistics, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Ludwigstrasse 33, 80539, Munich, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Thomas", 
        "givenName": "Janek", 
        "id": "sg:person.012446322567.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012446322567.50"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Erlangen-Nuremberg", 
          "id": "https://www.grid.ac/institutes/grid.5330.5", 
          "name": [
            "Department of Medical Informatics, Biometry and Epidemiology, FAU Erlangen-N\u00fcrnberg, Erlangen, Germany", 
            "Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mayr", 
        "givenName": "Andreas", 
        "id": "sg:person.0607252074.00", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0607252074.00"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Ludwig Maximilian University of Munich", 
          "id": "https://www.grid.ac/institutes/grid.5252.0", 
          "name": [
            "Department of Statistics, Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen, Ludwigstrasse 33, 80539, Munich, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bischl", 
        "givenName": "Bernd", 
        "id": "sg:person.010620043010.96", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010620043010.96"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Schmid", 
        "givenName": "Matthias", 
        "id": "sg:person.0745220772.74", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0745220772.74"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "U.S. Fish & Wildlife Service, National Wildlife Refuge System, Southeast Inventory & Monitoring Branch, Lewistown, MT, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Smith", 
        "givenName": "Adam", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Paul Ehrlich Institut", 
          "id": "https://www.grid.ac/institutes/grid.425396.f", 
          "name": [
            "Section Biostatistics, Paul-Ehrlich-Institute, Langen, 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"
      }
    ], 
    "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": "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/10.1016/j.jhydrol.2012.05.029", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004510627"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jhydrol.2012.05.029", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004510627"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/mwr-d-16-0088.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007050700"
        ], 
        "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.1461-0248.2009.01361.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014492154"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1461-0248.2009.01361.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014492154"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1214/aos/1016218223", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020629296"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/10-0602.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020740452"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1890/13-1452.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021607553"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1600-0587.2012.07348.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022525509"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0304-4076(86)90002-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027975208"
        ], 
        "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": "sg:pub.10.1007/s11222-009-9162-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032904741", 
          "https://doi.org/10.1007/s11222-009-9162-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11222-009-9162-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032904741", 
          "https://doi.org/10.1007/s11222-009-9162-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11222-009-9162-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032904741", 
          "https://doi.org/10.1007/s11222-009-9162-7"
        ], 
        "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": "sg:pub.10.1186/s12859-016-1149-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043698212", 
          "https://doi.org/10.1186/s12859-016-1149-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s12859-016-1149-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043698212", 
          "https://doi.org/10.1186/s12859-016-1149-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11222-009-9148-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044952906", 
          "https://doi.org/10.1007/s11222-009-9148-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11222-009-9148-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044952906", 
          "https://doi.org/10.1007/s11222-009-9148-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-24671-8_6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046580260", 
          "https://doi.org/10.1007/978-3-540-24671-8_6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-24671-8_6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046580260", 
          "https://doi.org/10.1007/978-3-540-24671-8_6"
        ], 
        "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/10.1017/s0016672312000419", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053943769"
        ], 
        "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/jcgs.2011.09220", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064201123"
        ], 
        "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/10.2307/3803155", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070459680"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3414/13100122", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071311911"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3414/me11-02-0030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071312140"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3414/me13-01-0122", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071312232"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3414/me13-01-0123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1071312233"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-05", 
    "datePublishedReg": "2018-05-01", 
    "description": "We present a new algorithm for boosting generalized additive models for location, scale and shape (GAMLSS) that allows to incorporate stability selection, an increasingly popular way to obtain stable sets of covariates while controlling the per-family error rate. The model is fitted repeatedly to subsampled data, and variables with high selection frequencies are extracted. To apply stability selection to boosted GAMLSS, we develop a new \u201cnoncyclical\u201d fitting algorithm that incorporates an additional selection step of the best-fitting distribution parameter in each iteration. This new algorithm has the additional advantage that optimizing the tuning parameters of boosting is reduced from a multi-dimensional to a one-dimensional problem with vastly decreased complexity. The performance of the novel algorithm is evaluated in an extensive simulation study. We apply this new algorithm to a study to estimate abundance of common eider in Massachusetts, USA, featuring excess zeros, overdispersion, nonlinearity and spatiotemporal structures. Eider abundance is estimated via boosted GAMLSS, allowing both mean and overdispersion to be regressed on covariates. Stability selection is used to obtain a sparse set of stable predictors.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11222-017-9754-6", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1327447", 
        "issn": [
          "0960-3174", 
          "1573-1375"
        ], 
        "name": "Statistics and Computing", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "28"
      }
    ], 
    "name": "Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates", 
    "pagination": "673-687", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "c6ffe6d0f3909656f997a54ddaf71453dd16c7c6f5d6ce57118f2e0cac179b16"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11222-017-9754-6"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1085396828"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11222-017-9754-6", 
      "https://app.dimensions.ai/details/publication/pub.1085396828"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T10:21", 
    "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/v90/0000000348_0000000348/records_54338_00000001.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs11222-017-9754-6"
  }
]
 

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/s11222-017-9754-6'

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/s11222-017-9754-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11222-017-9754-6'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11222-017-9754-6'


 

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

199 TRIPLES      21 PREDICATES      56 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11222-017-9754-6 schema:about anzsrc-for:01
2 anzsrc-for:0103
3 schema:author N571da90a091b4841953530742adf4413
4 schema:citation sg:pub.10.1007/978-3-540-24671-8_6
5 sg:pub.10.1007/s00180-012-0382-5
6 sg:pub.10.1007/s11222-009-9148-5
7 sg:pub.10.1007/s11222-009-9162-7
8 sg:pub.10.1186/s12859-015-0575-3
9 sg:pub.10.1186/s12859-016-1149-8
10 https://doi.org/10.1016/0304-4076(86)90002-3
11 https://doi.org/10.1016/j.csda.2008.09.009
12 https://doi.org/10.1016/j.jhydrol.2012.05.029
13 https://doi.org/10.1017/s0016672312000419
14 https://doi.org/10.1111/j.1461-0248.2009.01361.x
15 https://doi.org/10.1111/j.1467-9868.2010.00740.x
16 https://doi.org/10.1111/j.1467-9868.2011.01034.x
17 https://doi.org/10.1111/j.1467-9876.2005.00510.x
18 https://doi.org/10.1111/j.1467-9876.2011.01033.x
19 https://doi.org/10.1111/j.1600-0587.2012.07348.x
20 https://doi.org/10.1175/mwr-d-16-0088.1
21 https://doi.org/10.1198/016214503000125
22 https://doi.org/10.1198/jcgs.2011.09220
23 https://doi.org/10.1214/07-sts242
24 https://doi.org/10.1214/aos/1016218223
25 https://doi.org/10.18637/jss.v074.i01
26 https://doi.org/10.1890/10-0602.1
27 https://doi.org/10.1890/13-1452.1
28 https://doi.org/10.2307/3803155
29 https://doi.org/10.3414/13100122
30 https://doi.org/10.3414/me11-02-0030
31 https://doi.org/10.3414/me13-01-0122
32 https://doi.org/10.3414/me13-01-0123
33 schema:datePublished 2018-05
34 schema:datePublishedReg 2018-05-01
35 schema:description We present a new algorithm for boosting generalized additive models for location, scale and shape (GAMLSS) that allows to incorporate stability selection, an increasingly popular way to obtain stable sets of covariates while controlling the per-family error rate. The model is fitted repeatedly to subsampled data, and variables with high selection frequencies are extracted. To apply stability selection to boosted GAMLSS, we develop a new “noncyclical” fitting algorithm that incorporates an additional selection step of the best-fitting distribution parameter in each iteration. This new algorithm has the additional advantage that optimizing the tuning parameters of boosting is reduced from a multi-dimensional to a one-dimensional problem with vastly decreased complexity. The performance of the novel algorithm is evaluated in an extensive simulation study. We apply this new algorithm to a study to estimate abundance of common eider in Massachusetts, USA, featuring excess zeros, overdispersion, nonlinearity and spatiotemporal structures. Eider abundance is estimated via boosted GAMLSS, allowing both mean and overdispersion to be regressed on covariates. Stability selection is used to obtain a sparse set of stable predictors.
36 schema:genre research_article
37 schema:inLanguage en
38 schema:isAccessibleForFree true
39 schema:isPartOf N6933a71b276e4af880658ad3d27df652
40 Na211849634ce44c69cfb1ab0d51d26b9
41 sg:journal.1327447
42 schema:name Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates
43 schema:pagination 673-687
44 schema:productId N394c2141a7ae46d9934d5c73508b7b38
45 N8851245c8f2a4eed92bd2124438fb490
46 Nc0dd7e6e5da3487ebe13b4a88321dca7
47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085396828
48 https://doi.org/10.1007/s11222-017-9754-6
49 schema:sdDatePublished 2019-04-11T10:21
50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
51 schema:sdPublisher N7b049840129c4d62b7306b943cc3947e
52 schema:url https://link.springer.com/10.1007%2Fs11222-017-9754-6
53 sgo:license sg:explorer/license/
54 sgo:sdDataset articles
55 rdf:type schema:ScholarlyArticle
56 N394c2141a7ae46d9934d5c73508b7b38 schema:name dimensions_id
57 schema:value pub.1085396828
58 rdf:type schema:PropertyValue
59 N444d3d54c5554050ada480be01377406 schema:name U.S. Fish & Wildlife Service, National Wildlife Refuge System, Southeast Inventory & Monitoring Branch, Lewistown, MT, USA
60 rdf:type schema:Organization
61 N555169cdd5ea47d5a6656bc5b716c70d rdf:first N6ca22444220e4bb886b8274efa49d26a
62 rdf:rest Nf16efe1a3dde49d4a98d22a11ad52ec8
63 N571da90a091b4841953530742adf4413 rdf:first sg:person.012446322567.50
64 rdf:rest N6119bb746ac44c3c916254f565a54f3a
65 N6119bb746ac44c3c916254f565a54f3a rdf:first sg:person.0607252074.00
66 rdf:rest N65c3bd06f87040f3b6960ddc04770523
67 N65c3bd06f87040f3b6960ddc04770523 rdf:first sg:person.010620043010.96
68 rdf:rest Ne2d868feb5c345da9d8b2dabb36d6984
69 N6933a71b276e4af880658ad3d27df652 schema:volumeNumber 28
70 rdf:type schema:PublicationVolume
71 N6ca22444220e4bb886b8274efa49d26a schema:affiliation N444d3d54c5554050ada480be01377406
72 schema:familyName Smith
73 schema:givenName Adam
74 rdf:type schema:Person
75 N771176a9b396420499d5b7768e853499 schema:name Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
76 rdf:type schema:Organization
77 N7b049840129c4d62b7306b943cc3947e schema:name Springer Nature - SN SciGraph project
78 rdf:type schema:Organization
79 N8851245c8f2a4eed92bd2124438fb490 schema:name doi
80 schema:value 10.1007/s11222-017-9754-6
81 rdf:type schema:PropertyValue
82 Na211849634ce44c69cfb1ab0d51d26b9 schema:issueNumber 3
83 rdf:type schema:PublicationIssue
84 Nc0dd7e6e5da3487ebe13b4a88321dca7 schema:name readcube_id
85 schema:value c6ffe6d0f3909656f997a54ddaf71453dd16c7c6f5d6ce57118f2e0cac179b16
86 rdf:type schema:PropertyValue
87 Ne2d868feb5c345da9d8b2dabb36d6984 rdf:first sg:person.0745220772.74
88 rdf:rest N555169cdd5ea47d5a6656bc5b716c70d
89 Nf16efe1a3dde49d4a98d22a11ad52ec8 rdf:first sg:person.01044432226.13
90 rdf:rest rdf:nil
91 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
92 schema:name Mathematical Sciences
93 rdf:type schema:DefinedTerm
94 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
95 schema:name Numerical and Computational Mathematics
96 rdf:type schema:DefinedTerm
97 sg:journal.1327447 schema:issn 0960-3174
98 1573-1375
99 schema:name Statistics and Computing
100 rdf:type schema:Periodical
101 sg:person.01044432226.13 schema:affiliation https://www.grid.ac/institutes/grid.425396.f
102 schema:familyName Hofner
103 schema:givenName Benjamin
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01044432226.13
105 rdf:type schema:Person
106 sg:person.010620043010.96 schema:affiliation https://www.grid.ac/institutes/grid.5252.0
107 schema:familyName Bischl
108 schema:givenName Bernd
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010620043010.96
110 rdf:type schema:Person
111 sg:person.012446322567.50 schema:affiliation https://www.grid.ac/institutes/grid.5252.0
112 schema:familyName Thomas
113 schema:givenName Janek
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012446322567.50
115 rdf:type schema:Person
116 sg:person.0607252074.00 schema:affiliation https://www.grid.ac/institutes/grid.5330.5
117 schema:familyName Mayr
118 schema:givenName Andreas
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0607252074.00
120 rdf:type schema:Person
121 sg:person.0745220772.74 schema:affiliation N771176a9b396420499d5b7768e853499
122 schema:familyName Schmid
123 schema:givenName Matthias
124 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0745220772.74
125 rdf:type schema:Person
126 sg:pub.10.1007/978-3-540-24671-8_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046580260
127 https://doi.org/10.1007/978-3-540-24671-8_6
128 rdf:type schema:CreativeWork
129 sg:pub.10.1007/s00180-012-0382-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001684018
130 https://doi.org/10.1007/s00180-012-0382-5
131 rdf:type schema:CreativeWork
132 sg:pub.10.1007/s11222-009-9148-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044952906
133 https://doi.org/10.1007/s11222-009-9148-5
134 rdf:type schema:CreativeWork
135 sg:pub.10.1007/s11222-009-9162-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032904741
136 https://doi.org/10.1007/s11222-009-9162-7
137 rdf:type schema:CreativeWork
138 sg:pub.10.1186/s12859-015-0575-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028801735
139 https://doi.org/10.1186/s12859-015-0575-3
140 rdf:type schema:CreativeWork
141 sg:pub.10.1186/s12859-016-1149-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043698212
142 https://doi.org/10.1186/s12859-016-1149-8
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1016/0304-4076(86)90002-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027975208
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1016/j.csda.2008.09.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047435658
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/j.jhydrol.2012.05.029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004510627
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1017/s0016672312000419 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053943769
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1111/j.1461-0248.2009.01361.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1014492154
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1111/j.1467-9868.2010.00740.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1000696823
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1111/j.1467-9868.2011.01034.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1012681255
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1111/j.1467-9876.2005.00510.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1043199556
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1111/j.1467-9876.2011.01033.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1051869147
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1111/j.1600-0587.2012.07348.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1022525509
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1175/mwr-d-16-0088.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007050700
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1198/016214503000125 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064198102
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1198/jcgs.2011.09220 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064201123
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1214/07-sts242 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049744920
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1214/aos/1016218223 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020629296
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/10.1890/10-0602.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020740452
177 rdf:type schema:CreativeWork
178 https://doi.org/10.1890/13-1452.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021607553
179 rdf:type schema:CreativeWork
180 https://doi.org/10.2307/3803155 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070459680
181 rdf:type schema:CreativeWork
182 https://doi.org/10.3414/13100122 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071311911
183 rdf:type schema:CreativeWork
184 https://doi.org/10.3414/me11-02-0030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071312140
185 rdf:type schema:CreativeWork
186 https://doi.org/10.3414/me13-01-0122 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071312232
187 rdf:type schema:CreativeWork
188 https://doi.org/10.3414/me13-01-0123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1071312233
189 rdf:type schema:CreativeWork
190 https://www.grid.ac/institutes/grid.425396.f schema:alternateName Paul Ehrlich Institut
191 schema:name Section Biostatistics, Paul-Ehrlich-Institute, Langen, Germany
192 rdf:type schema:Organization
193 https://www.grid.ac/institutes/grid.5252.0 schema:alternateName Ludwig Maximilian University of Munich
194 schema:name Department of Statistics, Ludwig-Maximilians-Universität München, Ludwigstrasse 33, 80539, Munich, Germany
195 rdf:type schema:Organization
196 https://www.grid.ac/institutes/grid.5330.5 schema:alternateName University of Erlangen-Nuremberg
197 schema:name Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
198 Department of Medical Informatics, Biometry and Epidemiology, FAU Erlangen-Nürnberg, Erlangen, Germany
199 rdf:type schema:Organization
 




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


...