Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approach Article Swipe
Qi Zheng
,
Hyokyoung G. Hong
,
Yi Li
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1111/biom.13122
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1111/biom.13122
Conditional screening approaches have emerged as a powerful alternative to the commonly used marginal screening, as they can identify marginally weak but conditionally important variables. However, most existing conditional screening methods need to fix the initial conditioning set, which may determine the ultimately selected variables. If the conditioning set is not properly chosen, the methods may produce false negatives and positives. Moreover, screening approaches typically need to involve tuning parameters and extra modeling steps in order to reach a final model. We propose a sequential conditioning approach by dynamically updating the conditioning set with an iterative selection process. We provide its theoretical properties under the framework of generalized linear models. Powered by an extended Bayesian information criterion as the stopping rule, the method will lead to a final model without the need to choose tuning parameters or threshold parameters. The practical utility of the proposed method is examined via extensive simulations and analysis of a real clinical study on predicting multiple myeloma patients’ response to treatment based on their genomic profiles.
Related Topics
Concepts
Computer science
Set (abstract data type)
Bayesian probability
False positive paradox
Bayesian information criterion
Conditional dependence
Conditional probability
Generalized linear model
Machine learning
Data mining
Mathematical optimization
Artificial intelligence
Mathematics
Statistics
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/biom.13122
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/biom.13122
- OA Status
- bronze
- Cited By
- 10
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2964685033
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2964685033Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1111/biom.13122Digital Object Identifier
- Title
-
Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-07-27Full publication date if available
- Authors
-
Qi Zheng, Hyokyoung G. Hong, Yi LiList of authors in order
- Landing page
-
https://doi.org/10.1111/biom.13122Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/biom.13122Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/biom.13122Direct OA link when available
- Concepts
-
Computer science, Set (abstract data type), Bayesian probability, False positive paradox, Bayesian information criterion, Conditional dependence, Conditional probability, Generalized linear model, Machine learning, Data mining, Mathematical optimization, Artificial intelligence, Mathematics, Statistics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 2, 2022: 4, 2020: 2Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2964685033 |
|---|---|
| doi | https://doi.org/10.1111/biom.13122 |
| ids.doi | https://doi.org/10.1111/biom.13122 |
| ids.mag | 2964685033 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/31350909 |
| ids.openalex | https://openalex.org/W2964685033 |
| fwci | 1.6035 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000465 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Algorithms |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D001499 |
| mesh[1].is_major_topic | False |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Bayes Theorem |
| mesh[2].qualifier_ui | Q000379 |
| mesh[2].descriptor_ui | D001699 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | methods |
| mesh[2].descriptor_name | Biometry |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D003198 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Computer Simulation |
| mesh[4].qualifier_ui | Q000706 |
| mesh[4].descriptor_ui | D020869 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | statistics & numerical data |
| mesh[4].descriptor_name | Gene Expression Profiling |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D006801 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Humans |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D016013 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Likelihood Functions |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D016014 |
| mesh[7].is_major_topic | True |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Linear Models |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D016015 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Logistic Models |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D015233 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Models, Statistical |
| mesh[10].qualifier_ui | Q000235 |
| mesh[10].descriptor_ui | D009101 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | genetics |
| mesh[10].descriptor_name | Multiple Myeloma |
| mesh[11].qualifier_ui | Q000628 |
| mesh[11].descriptor_ui | D009101 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | therapy |
| mesh[11].descriptor_name | Multiple Myeloma |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D000465 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Algorithms |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D001499 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Bayes Theorem |
| mesh[14].qualifier_ui | Q000379 |
| mesh[14].descriptor_ui | D001699 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | methods |
| mesh[14].descriptor_name | Biometry |
| mesh[15].qualifier_ui | |
| mesh[15].descriptor_ui | D003198 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | |
| mesh[15].descriptor_name | Computer Simulation |
| mesh[16].qualifier_ui | Q000706 |
| mesh[16].descriptor_ui | D020869 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | statistics & numerical data |
| mesh[16].descriptor_name | Gene Expression Profiling |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D006801 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | Humans |
| mesh[18].qualifier_ui | |
| mesh[18].descriptor_ui | D016013 |
| mesh[18].is_major_topic | False |
| mesh[18].qualifier_name | |
| mesh[18].descriptor_name | Likelihood Functions |
| mesh[19].qualifier_ui | |
| mesh[19].descriptor_ui | D016014 |
| mesh[19].is_major_topic | True |
| mesh[19].qualifier_name | |
| mesh[19].descriptor_name | Linear Models |
| mesh[20].qualifier_ui | |
| mesh[20].descriptor_ui | D016015 |
| mesh[20].is_major_topic | False |
| mesh[20].qualifier_name | |
| mesh[20].descriptor_name | Logistic Models |
| mesh[21].qualifier_ui | |
| mesh[21].descriptor_ui | D015233 |
| mesh[21].is_major_topic | False |
| mesh[21].qualifier_name | |
| mesh[21].descriptor_name | Models, Statistical |
| mesh[22].qualifier_ui | Q000235 |
| mesh[22].descriptor_ui | D009101 |
| mesh[22].is_major_topic | False |
| mesh[22].qualifier_name | genetics |
| mesh[22].descriptor_name | Multiple Myeloma |
| mesh[23].qualifier_ui | Q000628 |
| mesh[23].descriptor_ui | D009101 |
| mesh[23].is_major_topic | False |
| mesh[23].qualifier_name | therapy |
| mesh[23].descriptor_name | Multiple Myeloma |
| mesh[24].qualifier_ui | |
| mesh[24].descriptor_ui | D000465 |
| mesh[24].is_major_topic | False |
| mesh[24].qualifier_name | |
| mesh[24].descriptor_name | Algorithms |
| mesh[25].qualifier_ui | |
| mesh[25].descriptor_ui | D001499 |
| mesh[25].is_major_topic | False |
| mesh[25].qualifier_name | |
| mesh[25].descriptor_name | Bayes Theorem |
| mesh[26].qualifier_ui | Q000379 |
| mesh[26].descriptor_ui | D001699 |
| mesh[26].is_major_topic | False |
| mesh[26].qualifier_name | methods |
| mesh[26].descriptor_name | Biometry |
| mesh[27].qualifier_ui | |
| mesh[27].descriptor_ui | D003198 |
| mesh[27].is_major_topic | False |
| mesh[27].qualifier_name | |
| mesh[27].descriptor_name | Computer Simulation |
| mesh[28].qualifier_ui | Q000706 |
| mesh[28].descriptor_ui | D020869 |
| mesh[28].is_major_topic | False |
| mesh[28].qualifier_name | statistics & numerical data |
| mesh[28].descriptor_name | Gene Expression Profiling |
| mesh[29].qualifier_ui | |
| mesh[29].descriptor_ui | D006801 |
| mesh[29].is_major_topic | False |
| mesh[29].qualifier_name | |
| mesh[29].descriptor_name | Humans |
| mesh[30].qualifier_ui | |
| mesh[30].descriptor_ui | D016013 |
| mesh[30].is_major_topic | False |
| mesh[30].qualifier_name | |
| mesh[30].descriptor_name | Likelihood Functions |
| mesh[31].qualifier_ui | |
| mesh[31].descriptor_ui | D016014 |
| mesh[31].is_major_topic | True |
| mesh[31].qualifier_name | |
| mesh[31].descriptor_name | Linear Models |
| mesh[32].qualifier_ui | |
| mesh[32].descriptor_ui | D016015 |
| mesh[32].is_major_topic | False |
| mesh[32].qualifier_name | |
| mesh[32].descriptor_name | Logistic Models |
| mesh[33].qualifier_ui | |
| mesh[33].descriptor_ui | D015233 |
| mesh[33].is_major_topic | False |
| mesh[33].qualifier_name | |
| mesh[33].descriptor_name | Models, Statistical |
| mesh[34].qualifier_ui | Q000235 |
| mesh[34].descriptor_ui | D009101 |
| mesh[34].is_major_topic | False |
| mesh[34].qualifier_name | genetics |
| mesh[34].descriptor_name | Multiple Myeloma |
| mesh[35].qualifier_ui | Q000628 |
| mesh[35].descriptor_ui | D009101 |
| mesh[35].is_major_topic | False |
| mesh[35].qualifier_name | therapy |
| mesh[35].descriptor_name | Multiple Myeloma |
| type | article |
| title | Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approach |
| awards[0].id | https://openalex.org/G2090950843 |
| awards[0].funder_id | https://openalex.org/F4320332161 |
| awards[0].display_name | |
| awards[0].funder_award_id | U01CA209414 |
| awards[0].funder_display_name | National Institutes of Health |
| awards[1].id | https://openalex.org/G2998344287 |
| awards[1].funder_id | https://openalex.org/F4320332161 |
| awards[1].display_name | |
| awards[1].funder_award_id | R01AG056764 |
| awards[1].funder_display_name | National Institutes of Health |
| awards[2].id | https://openalex.org/G6746174689 |
| awards[2].funder_id | https://openalex.org/F4320306076 |
| awards[2].display_name | |
| awards[2].funder_award_id | DMS 1915099 |
| awards[2].funder_display_name | National Science Foundation |
| biblio.issue | 1 |
| biblio.volume | 76 |
| biblio.last_page | 60 |
| biblio.first_page | 47 |
| topics[0].id | https://openalex.org/T11235 |
| topics[0].field.id | https://openalex.org/fields/26 |
| topics[0].field.display_name | Mathematics |
| topics[0].score | 0.9972000122070312 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2613 |
| topics[0].subfield.display_name | Statistics and Probability |
| topics[0].display_name | Statistical Methods in Clinical Trials |
| topics[1].id | https://openalex.org/T10136 |
| topics[1].field.id | https://openalex.org/fields/26 |
| topics[1].field.display_name | Mathematics |
| topics[1].score | 0.996999979019165 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2613 |
| topics[1].subfield.display_name | Statistics and Probability |
| topics[1].display_name | Statistical Methods and Inference |
| topics[2].id | https://openalex.org/T10243 |
| topics[2].field.id | https://openalex.org/fields/26 |
| topics[2].field.display_name | Mathematics |
| topics[2].score | 0.9882000088691711 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2613 |
| topics[2].subfield.display_name | Statistics and Probability |
| topics[2].display_name | Statistical Methods and Bayesian Inference |
| funders[0].id | https://openalex.org/F4320306076 |
| funders[0].ror | https://ror.org/021nxhr62 |
| funders[0].display_name | National Science Foundation |
| funders[1].id | https://openalex.org/F4320332161 |
| funders[1].ror | https://ror.org/01cwqze88 |
| funders[1].display_name | National Institutes of Health |
| is_xpac | False |
| apc_list.value | 3710 |
| apc_list.currency | USD |
| apc_list.value_usd | 3710 |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6274981498718262 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C177264268 |
| concepts[1].level | 2 |
| concepts[1].score | 0.558902382850647 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[1].display_name | Set (abstract data type) |
| concepts[2].id | https://openalex.org/C107673813 |
| concepts[2].level | 2 |
| concepts[2].score | 0.4810214638710022 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q812534 |
| concepts[2].display_name | Bayesian probability |
| concepts[3].id | https://openalex.org/C64869954 |
| concepts[3].level | 2 |
| concepts[3].score | 0.46594345569610596 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1859747 |
| concepts[3].display_name | False positive paradox |
| concepts[4].id | https://openalex.org/C168136583 |
| concepts[4].level | 2 |
| concepts[4].score | 0.4477076530456543 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1988242 |
| concepts[4].display_name | Bayesian information criterion |
| concepts[5].id | https://openalex.org/C174920663 |
| concepts[5].level | 2 |
| concepts[5].score | 0.425446480512619 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q5159256 |
| concepts[5].display_name | Conditional dependence |
| concepts[6].id | https://openalex.org/C44492722 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4147118031978607 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q327069 |
| concepts[6].display_name | Conditional probability |
| concepts[7].id | https://openalex.org/C41587187 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4118092656135559 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1501882 |
| concepts[7].display_name | Generalized linear model |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.41157686710357666 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C124101348 |
| concepts[9].level | 1 |
| concepts[9].score | 0.377372145652771 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[9].display_name | Data mining |
| concepts[10].id | https://openalex.org/C126255220 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3632850646972656 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[10].display_name | Mathematical optimization |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3273792862892151 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.2916489243507385 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C105795698 |
| concepts[13].level | 1 |
| concepts[13].score | 0.1563338339328766 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[13].display_name | Statistics |
| concepts[14].id | https://openalex.org/C199360897 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[14].display_name | Programming language |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.6274981498718262 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/set |
| keywords[1].score | 0.558902382850647 |
| keywords[1].display_name | Set (abstract data type) |
| keywords[2].id | https://openalex.org/keywords/bayesian-probability |
| keywords[2].score | 0.4810214638710022 |
| keywords[2].display_name | Bayesian probability |
| keywords[3].id | https://openalex.org/keywords/false-positive-paradox |
| keywords[3].score | 0.46594345569610596 |
| keywords[3].display_name | False positive paradox |
| keywords[4].id | https://openalex.org/keywords/bayesian-information-criterion |
| keywords[4].score | 0.4477076530456543 |
| keywords[4].display_name | Bayesian information criterion |
| keywords[5].id | https://openalex.org/keywords/conditional-dependence |
| keywords[5].score | 0.425446480512619 |
| keywords[5].display_name | Conditional dependence |
| keywords[6].id | https://openalex.org/keywords/conditional-probability |
| keywords[6].score | 0.4147118031978607 |
| keywords[6].display_name | Conditional probability |
| keywords[7].id | https://openalex.org/keywords/generalized-linear-model |
| keywords[7].score | 0.4118092656135559 |
| keywords[7].display_name | Generalized linear model |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.41157686710357666 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/data-mining |
| keywords[9].score | 0.377372145652771 |
| keywords[9].display_name | Data mining |
| keywords[10].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[10].score | 0.3632850646972656 |
| keywords[10].display_name | Mathematical optimization |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.3273792862892151 |
| keywords[11].display_name | Artificial intelligence |
| keywords[12].id | https://openalex.org/keywords/mathematics |
| keywords[12].score | 0.2916489243507385 |
| keywords[12].display_name | Mathematics |
| keywords[13].id | https://openalex.org/keywords/statistics |
| keywords[13].score | 0.1563338339328766 |
| keywords[13].display_name | Statistics |
| language | en |
| locations[0].id | doi:10.1111/biom.13122 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S8265502 |
| locations[0].source.issn | 0006-341X, 1541-0420 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0006-341X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Biometrics |
| locations[0].source.host_organization | https://openalex.org/P4310311648 |
| locations[0].source.host_organization_name | Oxford University Press |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| locations[0].source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| locations[0].license | |
| locations[0].pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/biom.13122 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Biometrics |
| locations[0].landing_page_url | https://doi.org/10.1111/biom.13122 |
| locations[1].id | pmid:31350909 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Biometrics |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/31350909 |
| locations[2].id | pmh:oai:deepblue.lib.umich.edu:2027.42/154458 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400393 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | Deep Blue (University of Michigan) |
| locations[2].source.host_organization | https://openalex.org/I27837315 |
| locations[2].source.host_organization_name | University of Michigan–Ann Arbor |
| locations[2].source.host_organization_lineage | https://openalex.org/I27837315 |
| locations[2].license | |
| locations[2].pdf_url | https://hdl.handle.net/2027.42/154458 |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://hdl.handle.net/2027.42/154458 |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:7136011 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Biometrics |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/7136011 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5050439687 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4654-2801 |
| authorships[0].author.display_name | Qi Zheng |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I142740786 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky |
| authorships[0].institutions[0].id | https://openalex.org/I142740786 |
| authorships[0].institutions[0].ror | https://ror.org/01ckdn478 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I142740786 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | University of Louisville |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Qi Zheng |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky |
| authorships[1].author.id | https://openalex.org/A5003116913 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4280-6243 |
| authorships[1].author.display_name | Hyokyoung G. Hong |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I87216513 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Statistics and Probability, Michigan State University, East Lansing, Michigan |
| authorships[1].institutions[0].id | https://openalex.org/I87216513 |
| authorships[1].institutions[0].ror | https://ror.org/05hs6h993 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I87216513 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Michigan State University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Hyokyoung G. Hong |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Department of Statistics and Probability, Michigan State University, East Lansing, Michigan |
| authorships[2].author.id | https://openalex.org/A5100421736 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-1720-2760 |
| authorships[2].author.display_name | Yi Li |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I27837315 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Biostatistics, University of Michigan, Ann Arbor, Michigan |
| authorships[2].institutions[0].id | https://openalex.org/I27837315 |
| authorships[2].institutions[0].ror | https://ror.org/00jmfr291 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I27837315 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | University of Michigan |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Yi Li |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Biostatistics, University of Michigan, Ann Arbor, Michigan |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/biom.13122 |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approach |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11235 |
| primary_topic.field.id | https://openalex.org/fields/26 |
| primary_topic.field.display_name | Mathematics |
| primary_topic.score | 0.9972000122070312 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2613 |
| primary_topic.subfield.display_name | Statistics and Probability |
| primary_topic.display_name | Statistical Methods in Clinical Trials |
| related_works | https://openalex.org/W2972479235, https://openalex.org/W1985074092, https://openalex.org/W2900754349, https://openalex.org/W2145639415, https://openalex.org/W4250630220, https://openalex.org/W4384376816, https://openalex.org/W2018667718, https://openalex.org/W2041765120, https://openalex.org/W2073846761, https://openalex.org/W4210738546 |
| cited_by_count | 10 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 2 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 4 |
| counts_by_year[4].year | 2020 |
| counts_by_year[4].cited_by_count | 2 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1111/biom.13122 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S8265502 |
| best_oa_location.source.issn | 0006-341X, 1541-0420 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0006-341X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Biometrics |
| best_oa_location.source.host_organization | https://openalex.org/P4310311648 |
| best_oa_location.source.host_organization_name | Oxford University Press |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| best_oa_location.source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/biom.13122 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Biometrics |
| best_oa_location.landing_page_url | https://doi.org/10.1111/biom.13122 |
| primary_location.id | doi:10.1111/biom.13122 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S8265502 |
| primary_location.source.issn | 0006-341X, 1541-0420 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0006-341X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Biometrics |
| primary_location.source.host_organization | https://openalex.org/P4310311648 |
| primary_location.source.host_organization_name | Oxford University Press |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311648, https://openalex.org/P4310311647 |
| primary_location.source.host_organization_lineage_names | Oxford University Press, University of Oxford |
| primary_location.license | |
| primary_location.pdf_url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/biom.13122 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Biometrics |
| primary_location.landing_page_url | https://doi.org/10.1111/biom.13122 |
| publication_date | 2019-07-27 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W2063343795, https://openalex.org/W1635119602, https://openalex.org/W1989453064, https://openalex.org/W2952563653, https://openalex.org/W2053061982, https://openalex.org/W2312839476, https://openalex.org/W1482495168, https://openalex.org/W2074682976, https://openalex.org/W2154560360, https://openalex.org/W2016119924, https://openalex.org/W2147606185, https://openalex.org/W2142246398, https://openalex.org/W2148687964, https://openalex.org/W2463442068, https://openalex.org/W2026574448, https://openalex.org/W2327718296, https://openalex.org/W1749326457, https://openalex.org/W1963566461, https://openalex.org/W2274262898, https://openalex.org/W2803818599, https://openalex.org/W2134838492, https://openalex.org/W2081474178, https://openalex.org/W2040905895, https://openalex.org/W2206147190, https://openalex.org/W4301861531, https://openalex.org/W2171504422, https://openalex.org/W1997840761, https://openalex.org/W2135046866, https://openalex.org/W3122008423, https://openalex.org/W1972949866, https://openalex.org/W1965125844, https://openalex.org/W2147631685, https://openalex.org/W6682241100, https://openalex.org/W2029947124, https://openalex.org/W1656287659, https://openalex.org/W3098306969, https://openalex.org/W3102942031, https://openalex.org/W3105234854, https://openalex.org/W3103324688, https://openalex.org/W2150940164, https://openalex.org/W3098713891, https://openalex.org/W3100041486, https://openalex.org/W2997644599 |
| referenced_works_count | 43 |
| abstract_inverted_index.a | 7, 79, 84, 127, 155 |
| abstract_inverted_index.If | 46 |
| abstract_inverted_index.We | 82, 99 |
| abstract_inverted_index.an | 95, 113 |
| abstract_inverted_index.as | 6, 16, 118 |
| abstract_inverted_index.by | 88, 112 |
| abstract_inverted_index.in | 75 |
| abstract_inverted_index.is | 50, 147 |
| abstract_inverted_index.of | 107, 143, 154 |
| abstract_inverted_index.on | 159, 168 |
| abstract_inverted_index.or | 137 |
| abstract_inverted_index.to | 10, 33, 67, 77, 126, 133, 165 |
| abstract_inverted_index.The | 140 |
| abstract_inverted_index.and | 60, 71, 152 |
| abstract_inverted_index.but | 22 |
| abstract_inverted_index.can | 18 |
| abstract_inverted_index.fix | 34 |
| abstract_inverted_index.its | 101 |
| abstract_inverted_index.may | 40, 56 |
| abstract_inverted_index.not | 51 |
| abstract_inverted_index.set | 49, 93 |
| abstract_inverted_index.the | 11, 35, 42, 47, 54, 91, 105, 119, 122, 131, 144 |
| abstract_inverted_index.via | 149 |
| abstract_inverted_index.have | 4 |
| abstract_inverted_index.lead | 125 |
| abstract_inverted_index.most | 27 |
| abstract_inverted_index.need | 32, 66, 132 |
| abstract_inverted_index.real | 156 |
| abstract_inverted_index.set, | 38 |
| abstract_inverted_index.they | 17 |
| abstract_inverted_index.used | 13 |
| abstract_inverted_index.weak | 21 |
| abstract_inverted_index.will | 124 |
| abstract_inverted_index.with | 94 |
| abstract_inverted_index.based | 167 |
| abstract_inverted_index.extra | 72 |
| abstract_inverted_index.false | 58 |
| abstract_inverted_index.final | 80, 128 |
| abstract_inverted_index.model | 129 |
| abstract_inverted_index.order | 76 |
| abstract_inverted_index.reach | 78 |
| abstract_inverted_index.rule, | 121 |
| abstract_inverted_index.steps | 74 |
| abstract_inverted_index.study | 158 |
| abstract_inverted_index.their | 169 |
| abstract_inverted_index.under | 104 |
| abstract_inverted_index.which | 39 |
| abstract_inverted_index.choose | 134 |
| abstract_inverted_index.linear | 109 |
| abstract_inverted_index.method | 123, 146 |
| abstract_inverted_index.model. | 81 |
| abstract_inverted_index.tuning | 69, 135 |
| abstract_inverted_index.Powered | 111 |
| abstract_inverted_index.chosen, | 53 |
| abstract_inverted_index.emerged | 5 |
| abstract_inverted_index.genomic | 170 |
| abstract_inverted_index.initial | 36 |
| abstract_inverted_index.involve | 68 |
| abstract_inverted_index.methods | 31, 55 |
| abstract_inverted_index.models. | 110 |
| abstract_inverted_index.myeloma | 162 |
| abstract_inverted_index.produce | 57 |
| abstract_inverted_index.propose | 83 |
| abstract_inverted_index.provide | 100 |
| abstract_inverted_index.utility | 142 |
| abstract_inverted_index.without | 130 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Bayesian | 115 |
| abstract_inverted_index.However, | 26 |
| abstract_inverted_index.analysis | 153 |
| abstract_inverted_index.approach | 87 |
| abstract_inverted_index.clinical | 157 |
| abstract_inverted_index.commonly | 12 |
| abstract_inverted_index.examined | 148 |
| abstract_inverted_index.existing | 28 |
| abstract_inverted_index.extended | 114 |
| abstract_inverted_index.identify | 19 |
| abstract_inverted_index.marginal | 14 |
| abstract_inverted_index.modeling | 73 |
| abstract_inverted_index.multiple | 161 |
| abstract_inverted_index.powerful | 8 |
| abstract_inverted_index.process. | 98 |
| abstract_inverted_index.properly | 52 |
| abstract_inverted_index.proposed | 145 |
| abstract_inverted_index.response | 164 |
| abstract_inverted_index.selected | 44 |
| abstract_inverted_index.stopping | 120 |
| abstract_inverted_index.updating | 90 |
| abstract_inverted_index.Moreover, | 62 |
| abstract_inverted_index.criterion | 117 |
| abstract_inverted_index.determine | 41 |
| abstract_inverted_index.extensive | 150 |
| abstract_inverted_index.framework | 106 |
| abstract_inverted_index.important | 24 |
| abstract_inverted_index.iterative | 96 |
| abstract_inverted_index.negatives | 59 |
| abstract_inverted_index.practical | 141 |
| abstract_inverted_index.profiles. | 171 |
| abstract_inverted_index.screening | 2, 30, 63 |
| abstract_inverted_index.selection | 97 |
| abstract_inverted_index.threshold | 138 |
| abstract_inverted_index.treatment | 166 |
| abstract_inverted_index.typically | 65 |
| abstract_inverted_index.approaches | 3, 64 |
| abstract_inverted_index.marginally | 20 |
| abstract_inverted_index.parameters | 70, 136 |
| abstract_inverted_index.positives. | 61 |
| abstract_inverted_index.predicting | 160 |
| abstract_inverted_index.properties | 103 |
| abstract_inverted_index.screening, | 15 |
| abstract_inverted_index.sequential | 85 |
| abstract_inverted_index.ultimately | 43 |
| abstract_inverted_index.variables. | 25, 45 |
| abstract_inverted_index.Conditional | 1 |
| abstract_inverted_index.alternative | 9 |
| abstract_inverted_index.conditional | 29 |
| abstract_inverted_index.dynamically | 89 |
| abstract_inverted_index.generalized | 108 |
| abstract_inverted_index.information | 116 |
| abstract_inverted_index.parameters. | 139 |
| abstract_inverted_index.patients’ | 163 |
| abstract_inverted_index.simulations | 151 |
| abstract_inverted_index.theoretical | 102 |
| abstract_inverted_index.conditioning | 37, 48, 86, 92 |
| abstract_inverted_index.conditionally | 23 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5003116913 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I87216513 |
| citation_normalized_percentile.value | 0.84011741 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |