Globally Adaptive Longitudinal Quantile Regression With High Dimensional Compositional Covariates Article Swipe
Huijuan Ma
,
Qi Zheng
,
Zhumin Zhang
,
HuiChuan J. Lai
,
Limin Peng
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5705/ss.202021.0006
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5705/ss.202021.0006
In this work, we propose a longitudinal quantile regression framework that enables a robust characterization of heterogeneous covariate-response associations in the presence of high-dimensional compositional covariates and repeated measurements of both response and covariates. We develop a globally adaptive penalization procedure, which can consistently identify covariate sparsity patterns across a continuum set of quantile levels. The proposed estimation procedure properly aggregates longitudinal observations over time, and ensures the satisfaction of the sum-zero coefficient constraint that is needed for proper interpretation of the effects of compositional covariates. We establish the oracle rate of uniform convergence and weak convergence of the resulting estimators, and further justify the proposed uniform selector of the tuning parameter in terms of achieving global model selection consistency. We derive an efficient algorithm by incorporating existing R packages to facilitate stable and fast computation. Our extensive simulation studies confirm the theoretical findings. We apply the proposed method to a longitudinal study of cystic fibrosis children where the association between gut microbiome and other diet-related biomarkers is of interest.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5705/ss.202021.0006
- OA Status
- green
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200314306
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4200314306Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5705/ss.202021.0006Digital Object Identifier
- Title
-
Globally Adaptive Longitudinal Quantile Regression With High Dimensional Compositional CovariatesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-29Full publication date if available
- Authors
-
Huijuan Ma, Qi Zheng, Zhumin Zhang, HuiChuan J. Lai, Limin PengList of authors in order
- Landing page
-
https://doi.org/10.5705/ss.202021.0006Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://pmc.ncbi.nlm.nih.gov/articles/PMC10361693/pdf/nihms-1757788.pdfDirect OA link when available
- Concepts
-
Covariate, Estimator, Quantile regression, Quantile, Consistency (knowledge bases), Mathematics, Statistics, Computer science, Lasso (programming language), Regression, Econometrics, Artificial intelligence, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
12Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4200314306 |
|---|---|
| doi | https://doi.org/10.5705/ss.202021.0006 |
| ids.doi | https://doi.org/10.5705/ss.202021.0006 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/37483468 |
| ids.openalex | https://openalex.org/W4200314306 |
| fwci | 0.0 |
| type | article |
| title | Globally Adaptive Longitudinal Quantile Regression With High Dimensional Compositional Covariates |
| biblio.issue | Spec Issue |
| biblio.volume | 33 |
| biblio.last_page | 1318 |
| biblio.first_page | 1295 |
| topics[0].id | https://openalex.org/T10136 |
| topics[0].field.id | https://openalex.org/fields/26 |
| topics[0].field.display_name | Mathematics |
| topics[0].score | 0.9954000115394592 |
| 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 and Inference |
| topics[1].id | https://openalex.org/T12157 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9724000096321106 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Geochemistry and Geologic Mapping |
| topics[2].id | https://openalex.org/T11901 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9663000106811523 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Bayesian Methods and Mixture Models |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C119043178 |
| concepts[0].level | 2 |
| concepts[0].score | 0.939093828201294 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q320723 |
| concepts[0].display_name | Covariate |
| concepts[1].id | https://openalex.org/C185429906 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6817987561225891 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1130160 |
| concepts[1].display_name | Estimator |
| concepts[2].id | https://openalex.org/C63817138 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6727865934371948 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q3455889 |
| concepts[2].display_name | Quantile regression |
| concepts[3].id | https://openalex.org/C118671147 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6272374987602234 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q578714 |
| concepts[3].display_name | Quantile |
| concepts[4].id | https://openalex.org/C2776436953 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5803661942481995 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q5163215 |
| concepts[4].display_name | Consistency (knowledge bases) |
| concepts[5].id | https://openalex.org/C33923547 |
| concepts[5].level | 0 |
| concepts[5].score | 0.4741073250770569 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[5].display_name | Mathematics |
| concepts[6].id | https://openalex.org/C105795698 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4567313492298126 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[6].display_name | Statistics |
| concepts[7].id | https://openalex.org/C41008148 |
| concepts[7].level | 0 |
| concepts[7].score | 0.4549803137779236 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[7].display_name | Computer science |
| concepts[8].id | https://openalex.org/C37616216 |
| concepts[8].level | 2 |
| concepts[8].score | 0.44763147830963135 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q3218363 |
| concepts[8].display_name | Lasso (programming language) |
| concepts[9].id | https://openalex.org/C83546350 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4248208999633789 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1139051 |
| concepts[9].display_name | Regression |
| concepts[10].id | https://openalex.org/C149782125 |
| concepts[10].level | 1 |
| concepts[10].score | 0.41405385732650757 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[10].display_name | Econometrics |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.15472576022148132 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C136764020 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q466 |
| concepts[12].display_name | World Wide Web |
| keywords[0].id | https://openalex.org/keywords/covariate |
| keywords[0].score | 0.939093828201294 |
| keywords[0].display_name | Covariate |
| keywords[1].id | https://openalex.org/keywords/estimator |
| keywords[1].score | 0.6817987561225891 |
| keywords[1].display_name | Estimator |
| keywords[2].id | https://openalex.org/keywords/quantile-regression |
| keywords[2].score | 0.6727865934371948 |
| keywords[2].display_name | Quantile regression |
| keywords[3].id | https://openalex.org/keywords/quantile |
| keywords[3].score | 0.6272374987602234 |
| keywords[3].display_name | Quantile |
| keywords[4].id | https://openalex.org/keywords/consistency |
| keywords[4].score | 0.5803661942481995 |
| keywords[4].display_name | Consistency (knowledge bases) |
| keywords[5].id | https://openalex.org/keywords/mathematics |
| keywords[5].score | 0.4741073250770569 |
| keywords[5].display_name | Mathematics |
| keywords[6].id | https://openalex.org/keywords/statistics |
| keywords[6].score | 0.4567313492298126 |
| keywords[6].display_name | Statistics |
| keywords[7].id | https://openalex.org/keywords/computer-science |
| keywords[7].score | 0.4549803137779236 |
| keywords[7].display_name | Computer science |
| keywords[8].id | https://openalex.org/keywords/lasso |
| keywords[8].score | 0.44763147830963135 |
| keywords[8].display_name | Lasso (programming language) |
| keywords[9].id | https://openalex.org/keywords/regression |
| keywords[9].score | 0.4248208999633789 |
| keywords[9].display_name | Regression |
| keywords[10].id | https://openalex.org/keywords/econometrics |
| keywords[10].score | 0.41405385732650757 |
| keywords[10].display_name | Econometrics |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.15472576022148132 |
| keywords[11].display_name | Artificial intelligence |
| language | en |
| locations[0].id | doi:10.5705/ss.202021.0006 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S23408671 |
| locations[0].source.issn | 1017-0405, 1996-8507 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1017-0405 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Statistica Sinica |
| locations[0].source.host_organization | https://openalex.org/P4362561675 |
| locations[0].source.host_organization_name | Institute of Statistical Science |
| locations[0].source.host_organization_lineage | https://openalex.org/P4362561675 |
| locations[0].source.host_organization_lineage_names | Institute of Statistical Science |
| locations[0].license | |
| locations[0].pdf_url | |
| 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 | Statistica Sinica |
| locations[0].landing_page_url | https://doi.org/10.5705/ss.202021.0006 |
| locations[1].id | pmid:37483468 |
| 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 | Statistica Sinica |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/37483468 |
| locations[2].id | pmh:oai:pubmedcentral.nih.gov:10361693 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S2764455111 |
| 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 | PubMed Central |
| locations[2].source.host_organization | https://openalex.org/I1299303238 |
| locations[2].source.host_organization_name | National Institutes of Health |
| locations[2].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[2].license | |
| locations[2].pdf_url | https://pmc.ncbi.nlm.nih.gov/articles/PMC10361693/pdf/nihms-1757788.pdf |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Stat Sin |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/10361693 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5100569098 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-2790-7035 |
| authorships[0].author.display_name | Huijuan Ma |
| authorships[0].countries | CN, US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I135310074, https://openalex.org/I150468666, https://openalex.org/I66867065 |
| authorships[0].affiliations[0].raw_affiliation_string | East China Normal University, University of Louiswille, University of Wisconsin-Madison, University of Wisconsin-Madison, Emory University. |
| authorships[0].institutions[0].id | https://openalex.org/I66867065 |
| authorships[0].institutions[0].ror | https://ror.org/02n96ep67 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I66867065 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | East China Normal University |
| authorships[0].institutions[1].id | https://openalex.org/I150468666 |
| authorships[0].institutions[1].ror | https://ror.org/03czfpz43 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I150468666 |
| authorships[0].institutions[1].country_code | US |
| authorships[0].institutions[1].display_name | Emory University |
| authorships[0].institutions[2].id | https://openalex.org/I135310074 |
| authorships[0].institutions[2].ror | https://ror.org/01y2jtd41 |
| authorships[0].institutions[2].type | education |
| authorships[0].institutions[2].lineage | https://openalex.org/I135310074 |
| authorships[0].institutions[2].country_code | US |
| authorships[0].institutions[2].display_name | University of Wisconsin–Madison |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Huijuan Ma |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | East China Normal University, University of Louiswille, University of Wisconsin-Madison, University of Wisconsin-Madison, Emory University. |
| authorships[1].author.id | https://openalex.org/A5050439687 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4654-2801 |
| authorships[1].author.display_name | Qi Zheng |
| authorships[1].countries | CN, US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I135310074, https://openalex.org/I150468666, https://openalex.org/I66867065 |
| authorships[1].affiliations[0].raw_affiliation_string | East China Normal University, University of Louiswille, University of Wisconsin-Madison, University of Wisconsin-Madison, Emory University. |
| authorships[1].institutions[0].id | https://openalex.org/I66867065 |
| authorships[1].institutions[0].ror | https://ror.org/02n96ep67 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I66867065 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | East China Normal University |
| authorships[1].institutions[1].id | https://openalex.org/I150468666 |
| authorships[1].institutions[1].ror | https://ror.org/03czfpz43 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I150468666 |
| authorships[1].institutions[1].country_code | US |
| authorships[1].institutions[1].display_name | Emory University |
| authorships[1].institutions[2].id | https://openalex.org/I135310074 |
| authorships[1].institutions[2].ror | https://ror.org/01y2jtd41 |
| authorships[1].institutions[2].type | education |
| authorships[1].institutions[2].lineage | https://openalex.org/I135310074 |
| authorships[1].institutions[2].country_code | US |
| authorships[1].institutions[2].display_name | University of Wisconsin–Madison |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Qi Zheng |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | East China Normal University, University of Louiswille, University of Wisconsin-Madison, University of Wisconsin-Madison, Emory University. |
| authorships[2].author.id | https://openalex.org/A5027657073 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-9780-5571 |
| authorships[2].author.display_name | Zhumin Zhang |
| authorships[2].countries | CN, US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I135310074, https://openalex.org/I150468666, https://openalex.org/I66867065 |
| authorships[2].affiliations[0].raw_affiliation_string | East China Normal University, University of Louiswille, University of Wisconsin-Madison, University of Wisconsin-Madison, Emory University. |
| authorships[2].institutions[0].id | https://openalex.org/I66867065 |
| authorships[2].institutions[0].ror | https://ror.org/02n96ep67 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I66867065 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | East China Normal University |
| authorships[2].institutions[1].id | https://openalex.org/I150468666 |
| authorships[2].institutions[1].ror | https://ror.org/03czfpz43 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I150468666 |
| authorships[2].institutions[1].country_code | US |
| authorships[2].institutions[1].display_name | Emory University |
| authorships[2].institutions[2].id | https://openalex.org/I135310074 |
| authorships[2].institutions[2].ror | https://ror.org/01y2jtd41 |
| authorships[2].institutions[2].type | education |
| authorships[2].institutions[2].lineage | https://openalex.org/I135310074 |
| authorships[2].institutions[2].country_code | US |
| authorships[2].institutions[2].display_name | University of Wisconsin–Madison |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Zhumin Zhang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | East China Normal University, University of Louiswille, University of Wisconsin-Madison, University of Wisconsin-Madison, Emory University. |
| authorships[3].author.id | https://openalex.org/A5062507785 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-8084-3988 |
| authorships[3].author.display_name | HuiChuan J. Lai |
| authorships[3].countries | CN, US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I135310074, https://openalex.org/I150468666, https://openalex.org/I66867065 |
| authorships[3].affiliations[0].raw_affiliation_string | East China Normal University, University of Louiswille, University of Wisconsin-Madison, University of Wisconsin-Madison, Emory University. |
| authorships[3].institutions[0].id | https://openalex.org/I66867065 |
| authorships[3].institutions[0].ror | https://ror.org/02n96ep67 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I66867065 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | East China Normal University |
| authorships[3].institutions[1].id | https://openalex.org/I150468666 |
| authorships[3].institutions[1].ror | https://ror.org/03czfpz43 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I150468666 |
| authorships[3].institutions[1].country_code | US |
| authorships[3].institutions[1].display_name | Emory University |
| authorships[3].institutions[2].id | https://openalex.org/I135310074 |
| authorships[3].institutions[2].ror | https://ror.org/01y2jtd41 |
| authorships[3].institutions[2].type | education |
| authorships[3].institutions[2].lineage | https://openalex.org/I135310074 |
| authorships[3].institutions[2].country_code | US |
| authorships[3].institutions[2].display_name | University of Wisconsin–Madison |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Huichuan Lai |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | East China Normal University, University of Louiswille, University of Wisconsin-Madison, University of Wisconsin-Madison, Emory University. |
| authorships[4].author.id | https://openalex.org/A5086554574 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-8042-3112 |
| authorships[4].author.display_name | Limin Peng |
| authorships[4].countries | CN, US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I135310074, https://openalex.org/I150468666, https://openalex.org/I66867065 |
| authorships[4].affiliations[0].raw_affiliation_string | East China Normal University, University of Louiswille, University of Wisconsin-Madison, University of Wisconsin-Madison, Emory University. |
| authorships[4].institutions[0].id | https://openalex.org/I66867065 |
| authorships[4].institutions[0].ror | https://ror.org/02n96ep67 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I66867065 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | East China Normal University |
| authorships[4].institutions[1].id | https://openalex.org/I150468666 |
| authorships[4].institutions[1].ror | https://ror.org/03czfpz43 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I150468666 |
| authorships[4].institutions[1].country_code | US |
| authorships[4].institutions[1].display_name | Emory University |
| authorships[4].institutions[2].id | https://openalex.org/I135310074 |
| authorships[4].institutions[2].ror | https://ror.org/01y2jtd41 |
| authorships[4].institutions[2].type | education |
| authorships[4].institutions[2].lineage | https://openalex.org/I135310074 |
| authorships[4].institutions[2].country_code | US |
| authorships[4].institutions[2].display_name | University of Wisconsin–Madison |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Limin Peng |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | East China Normal University, University of Louiswille, University of Wisconsin-Madison, University of Wisconsin-Madison, Emory University. |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://pmc.ncbi.nlm.nih.gov/articles/PMC10361693/pdf/nihms-1757788.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Globally Adaptive Longitudinal Quantile Regression With High Dimensional Compositional Covariates |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10136 |
| primary_topic.field.id | https://openalex.org/fields/26 |
| primary_topic.field.display_name | Mathematics |
| primary_topic.score | 0.9954000115394592 |
| 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 and Inference |
| related_works | https://openalex.org/W194268815, https://openalex.org/W4226533060, https://openalex.org/W4319989324, https://openalex.org/W4319299633, https://openalex.org/W4213413690, https://openalex.org/W3123305356, https://openalex.org/W4205884682, https://openalex.org/W1907667133, https://openalex.org/W1923266891, https://openalex.org/W3121490443 |
| cited_by_count | 0 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:pubmedcentral.nih.gov:10361693 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764455111 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | PubMed Central |
| best_oa_location.source.host_organization | https://openalex.org/I1299303238 |
| best_oa_location.source.host_organization_name | National Institutes of Health |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://pmc.ncbi.nlm.nih.gov/articles/PMC10361693/pdf/nihms-1757788.pdf |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | Stat Sin |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/10361693 |
| primary_location.id | doi:10.5705/ss.202021.0006 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S23408671 |
| primary_location.source.issn | 1017-0405, 1996-8507 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1017-0405 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Statistica Sinica |
| primary_location.source.host_organization | https://openalex.org/P4362561675 |
| primary_location.source.host_organization_name | Institute of Statistical Science |
| primary_location.source.host_organization_lineage | https://openalex.org/P4362561675 |
| primary_location.source.host_organization_lineage_names | Institute of Statistical Science |
| primary_location.license | |
| primary_location.pdf_url | |
| 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 | Statistica Sinica |
| primary_location.landing_page_url | https://doi.org/10.5705/ss.202021.0006 |
| publication_date | 2021-11-29 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2137620021, https://openalex.org/W2962797912, https://openalex.org/W2947461492, https://openalex.org/W2133593515, https://openalex.org/W2054358221, https://openalex.org/W1503352355, https://openalex.org/W3101498811, https://openalex.org/W1656287659, https://openalex.org/W2002316706, https://openalex.org/W1938840888, https://openalex.org/W1485174478, https://openalex.org/W2329918667 |
| referenced_works_count | 12 |
| abstract_inverted_index.R | 128 |
| abstract_inverted_index.a | 5, 12, 36, 49, 150 |
| abstract_inverted_index.In | 0 |
| abstract_inverted_index.We | 34, 86, 120, 144 |
| abstract_inverted_index.an | 122 |
| abstract_inverted_index.by | 125 |
| abstract_inverted_index.in | 19, 112 |
| abstract_inverted_index.is | 75, 167 |
| abstract_inverted_index.of | 15, 22, 29, 52, 69, 80, 83, 91, 97, 108, 114, 153, 168 |
| abstract_inverted_index.to | 130, 149 |
| abstract_inverted_index.we | 3 |
| abstract_inverted_index.Our | 136 |
| abstract_inverted_index.The | 55 |
| abstract_inverted_index.and | 26, 32, 65, 94, 101, 133, 163 |
| abstract_inverted_index.can | 42 |
| abstract_inverted_index.for | 77 |
| abstract_inverted_index.gut | 161 |
| abstract_inverted_index.set | 51 |
| abstract_inverted_index.the | 20, 67, 70, 81, 88, 98, 104, 109, 141, 146, 158 |
| abstract_inverted_index.both | 30 |
| abstract_inverted_index.fast | 134 |
| abstract_inverted_index.over | 63 |
| abstract_inverted_index.rate | 90 |
| abstract_inverted_index.that | 10, 74 |
| abstract_inverted_index.this | 1 |
| abstract_inverted_index.weak | 95 |
| abstract_inverted_index.apply | 145 |
| abstract_inverted_index.model | 117 |
| abstract_inverted_index.other | 164 |
| abstract_inverted_index.study | 152 |
| abstract_inverted_index.terms | 113 |
| abstract_inverted_index.time, | 64 |
| abstract_inverted_index.where | 157 |
| abstract_inverted_index.which | 41 |
| abstract_inverted_index.work, | 2 |
| abstract_inverted_index.across | 48 |
| abstract_inverted_index.cystic | 154 |
| abstract_inverted_index.derive | 121 |
| abstract_inverted_index.global | 116 |
| abstract_inverted_index.method | 148 |
| abstract_inverted_index.needed | 76 |
| abstract_inverted_index.oracle | 89 |
| abstract_inverted_index.proper | 78 |
| abstract_inverted_index.robust | 13 |
| abstract_inverted_index.stable | 132 |
| abstract_inverted_index.tuning | 110 |
| abstract_inverted_index.between | 160 |
| abstract_inverted_index.confirm | 140 |
| abstract_inverted_index.develop | 35 |
| abstract_inverted_index.effects | 82 |
| abstract_inverted_index.enables | 11 |
| abstract_inverted_index.ensures | 66 |
| abstract_inverted_index.further | 102 |
| abstract_inverted_index.justify | 103 |
| abstract_inverted_index.levels. | 54 |
| abstract_inverted_index.propose | 4 |
| abstract_inverted_index.studies | 139 |
| abstract_inverted_index.uniform | 92, 106 |
| abstract_inverted_index.adaptive | 38 |
| abstract_inverted_index.children | 156 |
| abstract_inverted_index.existing | 127 |
| abstract_inverted_index.fibrosis | 155 |
| abstract_inverted_index.globally | 37 |
| abstract_inverted_index.identify | 44 |
| abstract_inverted_index.packages | 129 |
| abstract_inverted_index.patterns | 47 |
| abstract_inverted_index.presence | 21 |
| abstract_inverted_index.properly | 59 |
| abstract_inverted_index.proposed | 56, 105, 147 |
| abstract_inverted_index.quantile | 7, 53 |
| abstract_inverted_index.repeated | 27 |
| abstract_inverted_index.response | 31 |
| abstract_inverted_index.selector | 107 |
| abstract_inverted_index.sparsity | 46 |
| abstract_inverted_index.sum-zero | 71 |
| abstract_inverted_index.achieving | 115 |
| abstract_inverted_index.algorithm | 124 |
| abstract_inverted_index.continuum | 50 |
| abstract_inverted_index.covariate | 45 |
| abstract_inverted_index.efficient | 123 |
| abstract_inverted_index.establish | 87 |
| abstract_inverted_index.extensive | 137 |
| abstract_inverted_index.findings. | 143 |
| abstract_inverted_index.framework | 9 |
| abstract_inverted_index.interest. | 169 |
| abstract_inverted_index.parameter | 111 |
| abstract_inverted_index.procedure | 58 |
| abstract_inverted_index.resulting | 99 |
| abstract_inverted_index.selection | 118 |
| abstract_inverted_index.aggregates | 60 |
| abstract_inverted_index.biomarkers | 166 |
| abstract_inverted_index.constraint | 73 |
| abstract_inverted_index.covariates | 25 |
| abstract_inverted_index.estimation | 57 |
| abstract_inverted_index.facilitate | 131 |
| abstract_inverted_index.microbiome | 162 |
| abstract_inverted_index.procedure, | 40 |
| abstract_inverted_index.regression | 8 |
| abstract_inverted_index.simulation | 138 |
| abstract_inverted_index.association | 159 |
| abstract_inverted_index.coefficient | 72 |
| abstract_inverted_index.convergence | 93, 96 |
| abstract_inverted_index.covariates. | 33, 85 |
| abstract_inverted_index.estimators, | 100 |
| abstract_inverted_index.theoretical | 142 |
| abstract_inverted_index.associations | 18 |
| abstract_inverted_index.computation. | 135 |
| abstract_inverted_index.consistency. | 119 |
| abstract_inverted_index.consistently | 43 |
| abstract_inverted_index.diet-related | 165 |
| abstract_inverted_index.longitudinal | 6, 61, 151 |
| abstract_inverted_index.measurements | 28 |
| abstract_inverted_index.observations | 62 |
| abstract_inverted_index.penalization | 39 |
| abstract_inverted_index.satisfaction | 68 |
| abstract_inverted_index.compositional | 24, 84 |
| abstract_inverted_index.heterogeneous | 16 |
| abstract_inverted_index.incorporating | 126 |
| abstract_inverted_index.interpretation | 79 |
| abstract_inverted_index.characterization | 14 |
| abstract_inverted_index.high-dimensional | 23 |
| abstract_inverted_index.covariate-response | 17 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.18029547 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |