Total effects with constrained features Article Swipe
Emanuele Borgonovo
,
Elmar Plischke
,
Clémentine Prieur
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s11222-024-10398-5
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s11222-024-10398-5
Recent studies have emphasized the connection between machine learning feature importance measures and total order sensitivity indices (total effects, henceforth). Feature correlations and the need to avoid unrestricted permutations make the estimation of these indices challenging. Additionally, there is no established theory or approach for non-Cartesian domains. We propose four alternative strategies for computing total effects that account for both dependent and constrained features. Our first approach involves a generalized winding stairs design combined with the Knothe-Rosenblatt transformation. This approach, while applicable to a wide family of input dependencies, becomes impractical when inputs are physically constrained. Our second approach is a U-statistic that combines the Jansen estimator with a weighting factor. The U-statistic framework allows the derivation of a central limit theorem for this estimator. However, this design is computationally intensive. Then, our third approach uses derangements to significantly reduce computational burden. We prove consistency and central limit theorems for these estimators as well. Our fourth approach is based on a nearest-neighbour intuition and it further reduces computational burden. We test these estimators through a series of increasingly complex computational experiments with features constrained on compact and connected domains (circle, simplex), non-compact and non-connected domains (Sierpinski gaskets), we provide comparisons with machine learning approaches and conclude with an application to a realistic simulator.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11222-024-10398-5
- https://link.springer.com/content/pdf/10.1007/s11222-024-10398-5.pdf
- OA Status
- hybrid
- Cited By
- 2
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378251990
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4378251990Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s11222-024-10398-5Digital Object Identifier
- Title
-
Total effects with constrained featuresWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-05Full publication date if available
- Authors
-
Emanuele Borgonovo, Elmar Plischke, Clémentine PrieurList of authors in order
- Landing page
-
https://doi.org/10.1007/s11222-024-10398-5Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s11222-024-10398-5.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s11222-024-10398-5.pdfDirect OA link when available
- Concepts
-
Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
58Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4378251990 |
|---|---|
| doi | https://doi.org/10.1007/s11222-024-10398-5 |
| ids.doi | https://doi.org/10.1007/s11222-024-10398-5 |
| ids.openalex | https://openalex.org/W4378251990 |
| fwci | 1.75631364 |
| type | article |
| title | Total effects with constrained features |
| biblio.issue | 2 |
| biblio.volume | 34 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10928 |
| topics[0].field.id | https://openalex.org/fields/18 |
| topics[0].field.display_name | Decision Sciences |
| topics[0].score | 0.9994000196456909 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1804 |
| topics[0].subfield.display_name | Statistics, Probability and Uncertainty |
| topics[0].display_name | Probabilistic and Robust Engineering Design |
| topics[1].id | https://openalex.org/T12404 |
| topics[1].field.id | https://openalex.org/fields/26 |
| topics[1].field.display_name | Mathematics |
| topics[1].score | 0.9987000226974487 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2612 |
| topics[1].subfield.display_name | Numerical Analysis |
| topics[1].display_name | Mathematical Approximation and Integration |
| topics[2].id | https://openalex.org/T10848 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9896000027656555 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1703 |
| topics[2].subfield.display_name | Computational Theory and Mathematics |
| topics[2].display_name | Advanced Multi-Objective Optimization Algorithms |
| is_xpac | False |
| apc_list.value | 2090 |
| apc_list.currency | EUR |
| apc_list.value_usd | 2690 |
| apc_paid.value | 2090 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2690 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.4384778141975403 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.4384778141975403 |
| keywords[0].display_name | Computer science |
| language | en |
| locations[0].id | doi:10.1007/s11222-024-10398-5 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S5437875 |
| locations[0].source.issn | 0960-3174, 1573-1375 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0960-3174 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Statistics and Computing |
| locations[0].source.host_organization | https://openalex.org/P4310319900 |
| locations[0].source.host_organization_name | Springer Science+Business Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://link.springer.com/content/pdf/10.1007/s11222-024-10398-5.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Statistics and Computing |
| locations[0].landing_page_url | https://doi.org/10.1007/s11222-024-10398-5 |
| locations[1].id | pmh:oai:HAL:hal-04102882v2 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306402512 |
| 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 | HAL (Le Centre pour la Communication Scientifique Directe) |
| locations[1].source.host_organization | https://openalex.org/I1294671590 |
| locations[1].source.host_organization_name | Centre National de la Recherche Scientifique |
| locations[1].source.host_organization_lineage | https://openalex.org/I1294671590 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | Journal articles |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Statistics and Computing, 2024, 34 (87), pp.1-29. ⟨10.1007/s11222-024-10398-5⟩ |
| locations[1].landing_page_url | https://inria.hal.science/hal-04102882 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5084219004 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8659-6017 |
| authorships[0].author.display_name | Emanuele Borgonovo |
| authorships[0].countries | IT |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I71209653 |
| authorships[0].affiliations[0].raw_affiliation_string | Bocconi Institute for Data Science and Analytics |
| authorships[0].institutions[0].id | https://openalex.org/I71209653 |
| authorships[0].institutions[0].ror | https://ror.org/05crjpb27 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I71209653 |
| authorships[0].institutions[0].country_code | IT |
| authorships[0].institutions[0].display_name | Bocconi University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Emanuele Borgonovo |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Bocconi Institute for Data Science and Analytics |
| authorships[1].author.id | https://openalex.org/A5047734891 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-2019-9243 |
| authorships[1].author.display_name | Elmar Plischke |
| authorships[1].countries | DE |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I43980791 |
| authorships[1].affiliations[0].raw_affiliation_string | Technische Universität Clausthal |
| authorships[1].institutions[0].id | https://openalex.org/I43980791 |
| authorships[1].institutions[0].ror | https://ror.org/04qb8nc58 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I43980791 |
| authorships[1].institutions[0].country_code | DE |
| authorships[1].institutions[0].display_name | Clausthal University of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Elmar Plischke |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Technische Universität Clausthal |
| authorships[2].author.id | https://openalex.org/A5110159432 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Clémentine Prieur |
| authorships[2].countries | US |
| authorships[2].affiliations[0].raw_affiliation_string | Méthodes d'Analyse Stochastique des Codes et Traitements Numériques |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I2802992173 |
| authorships[2].affiliations[1].raw_affiliation_string | Mathematics and computing applied to oceanic and atmospheric flows |
| authorships[2].institutions[0].id | https://openalex.org/I2802992173 |
| authorships[2].institutions[0].ror | https://ror.org/02kgve346 |
| authorships[2].institutions[0].type | government |
| authorships[2].institutions[0].lineage | https://openalex.org/I1308126019, https://openalex.org/I1343035065, https://openalex.org/I2802992173 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | NOAA Oceanic and Atmospheric Research |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Clémentine Prieur |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Mathematics and computing applied to oceanic and atmospheric flows, Méthodes d'Analyse Stochastique des Codes et Traitements Numériques |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://link.springer.com/content/pdf/10.1007/s11222-024-10398-5.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Total effects with constrained features |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10928 |
| primary_topic.field.id | https://openalex.org/fields/18 |
| primary_topic.field.display_name | Decision Sciences |
| primary_topic.score | 0.9994000196456909 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1804 |
| primary_topic.subfield.display_name | Statistics, Probability and Uncertainty |
| primary_topic.display_name | Probabilistic and Robust Engineering Design |
| related_works | https://openalex.org/W2748952813, https://openalex.org/W2390279801, https://openalex.org/W2358668433, https://openalex.org/W2376932109, https://openalex.org/W2001405890, https://openalex.org/W2382290278, https://openalex.org/W2478288626, https://openalex.org/W4391913857, https://openalex.org/W2350741829, https://openalex.org/W2530322880 |
| cited_by_count | 2 |
| 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 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1007/s11222-024-10398-5 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S5437875 |
| best_oa_location.source.issn | 0960-3174, 1573-1375 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0960-3174 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Statistics and Computing |
| best_oa_location.source.host_organization | https://openalex.org/P4310319900 |
| best_oa_location.source.host_organization_name | Springer Science+Business Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s11222-024-10398-5.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Statistics and Computing |
| best_oa_location.landing_page_url | https://doi.org/10.1007/s11222-024-10398-5 |
| primary_location.id | doi:10.1007/s11222-024-10398-5 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S5437875 |
| primary_location.source.issn | 0960-3174, 1573-1375 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0960-3174 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Statistics and Computing |
| primary_location.source.host_organization | https://openalex.org/P4310319900 |
| primary_location.source.host_organization_name | Springer Science+Business Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s11222-024-10398-5.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Statistics and Computing |
| primary_location.landing_page_url | https://doi.org/10.1007/s11222-024-10398-5 |
| publication_date | 2024-03-05 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2968153595, https://openalex.org/W4213420000, https://openalex.org/W2163417850, https://openalex.org/W2911964244, https://openalex.org/W2906011738, https://openalex.org/W2963371845, https://openalex.org/W1554947151, https://openalex.org/W2964075504, https://openalex.org/W3017541653, https://openalex.org/W3205981101, https://openalex.org/W139659980, https://openalex.org/W2805527161, https://openalex.org/W2145660559, https://openalex.org/W1992068214, https://openalex.org/W2798247711, https://openalex.org/W1938308181, https://openalex.org/W2018602967, https://openalex.org/W2295701977, https://openalex.org/W3081996497, https://openalex.org/W2963219711, https://openalex.org/W2166684980, https://openalex.org/W4234552994, https://openalex.org/W2026645785, https://openalex.org/W3209434414, https://openalex.org/W1994665973, https://openalex.org/W2065536623, https://openalex.org/W2060003311, https://openalex.org/W2006037565, https://openalex.org/W1524148033, https://openalex.org/W390686421, https://openalex.org/W2053411831, https://openalex.org/W2964283529, https://openalex.org/W1578330309, https://openalex.org/W2964060211, https://openalex.org/W2618368739, https://openalex.org/W6834628200, https://openalex.org/W3022464093, https://openalex.org/W2025175130, https://openalex.org/W2102300987, https://openalex.org/W2016770622, https://openalex.org/W2910705748, https://openalex.org/W2141507340, https://openalex.org/W2097441841, https://openalex.org/W3169055364, https://openalex.org/W2530685584, https://openalex.org/W2036887984, https://openalex.org/W2065082786, https://openalex.org/W4301751616, https://openalex.org/W79383899, https://openalex.org/W1995565517, https://openalex.org/W2063847981, https://openalex.org/W1973492181, https://openalex.org/W2325741025, https://openalex.org/W3081894066, https://openalex.org/W1460189015, https://openalex.org/W2751886678, https://openalex.org/W3211857494, https://openalex.org/W3024144868 |
| referenced_works_count | 58 |
| abstract_inverted_index.a | 69, 84, 101, 109, 119, 161, 175, 211 |
| abstract_inverted_index.We | 48, 143, 170 |
| abstract_inverted_index.an | 208 |
| abstract_inverted_index.as | 153 |
| abstract_inverted_index.is | 39, 100, 129, 158 |
| abstract_inverted_index.it | 165 |
| abstract_inverted_index.no | 40 |
| abstract_inverted_index.of | 33, 87, 118, 177 |
| abstract_inverted_index.on | 160, 185 |
| abstract_inverted_index.or | 43 |
| abstract_inverted_index.to | 26, 83, 138, 210 |
| abstract_inverted_index.we | 198 |
| abstract_inverted_index.Our | 65, 97, 155 |
| abstract_inverted_index.The | 112 |
| abstract_inverted_index.and | 13, 23, 62, 146, 164, 187, 193, 205 |
| abstract_inverted_index.are | 94 |
| abstract_inverted_index.for | 45, 53, 59, 123, 150 |
| abstract_inverted_index.our | 133 |
| abstract_inverted_index.the | 5, 24, 31, 76, 105, 116 |
| abstract_inverted_index.This | 79 |
| abstract_inverted_index.both | 60 |
| abstract_inverted_index.four | 50 |
| abstract_inverted_index.have | 3 |
| abstract_inverted_index.make | 30 |
| abstract_inverted_index.need | 25 |
| abstract_inverted_index.test | 171 |
| abstract_inverted_index.that | 57, 103 |
| abstract_inverted_index.this | 124, 127 |
| abstract_inverted_index.uses | 136 |
| abstract_inverted_index.when | 92 |
| abstract_inverted_index.wide | 85 |
| abstract_inverted_index.with | 75, 108, 182, 201, 207 |
| abstract_inverted_index.Then, | 132 |
| abstract_inverted_index.avoid | 27 |
| abstract_inverted_index.based | 159 |
| abstract_inverted_index.first | 66 |
| abstract_inverted_index.input | 88 |
| abstract_inverted_index.limit | 121, 148 |
| abstract_inverted_index.order | 15 |
| abstract_inverted_index.prove | 144 |
| abstract_inverted_index.there | 38 |
| abstract_inverted_index.these | 34, 151, 172 |
| abstract_inverted_index.third | 134 |
| abstract_inverted_index.total | 14, 55 |
| abstract_inverted_index.well. | 154 |
| abstract_inverted_index.while | 81 |
| abstract_inverted_index.(total | 18 |
| abstract_inverted_index.Jansen | 106 |
| abstract_inverted_index.Recent | 1 |
| abstract_inverted_index.allows | 115 |
| abstract_inverted_index.design | 73, 128 |
| abstract_inverted_index.family | 86 |
| abstract_inverted_index.fourth | 156 |
| abstract_inverted_index.inputs | 93 |
| abstract_inverted_index.reduce | 140 |
| abstract_inverted_index.second | 98 |
| abstract_inverted_index.series | 176 |
| abstract_inverted_index.stairs | 72 |
| abstract_inverted_index.theory | 42 |
| abstract_inverted_index.Feature | 21 |
| abstract_inverted_index.account | 58 |
| abstract_inverted_index.becomes | 90 |
| abstract_inverted_index.between | 7 |
| abstract_inverted_index.burden. | 142, 169 |
| abstract_inverted_index.central | 120, 147 |
| abstract_inverted_index.compact | 186 |
| abstract_inverted_index.complex | 179 |
| abstract_inverted_index.domains | 189, 195 |
| abstract_inverted_index.effects | 56 |
| abstract_inverted_index.factor. | 111 |
| abstract_inverted_index.feature | 10 |
| abstract_inverted_index.further | 166 |
| abstract_inverted_index.indices | 17, 35 |
| abstract_inverted_index.machine | 8, 202 |
| abstract_inverted_index.propose | 49 |
| abstract_inverted_index.provide | 199 |
| abstract_inverted_index.reduces | 167 |
| abstract_inverted_index.studies | 2 |
| abstract_inverted_index.theorem | 122 |
| abstract_inverted_index.through | 174 |
| abstract_inverted_index.winding | 71 |
| abstract_inverted_index.(circle, | 190 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 126 |
| abstract_inverted_index.approach | 44, 67, 99, 135, 157 |
| abstract_inverted_index.combined | 74 |
| abstract_inverted_index.combines | 104 |
| abstract_inverted_index.conclude | 206 |
| abstract_inverted_index.domains. | 47 |
| abstract_inverted_index.effects, | 19 |
| abstract_inverted_index.features | 183 |
| abstract_inverted_index.involves | 68 |
| abstract_inverted_index.learning | 9, 203 |
| abstract_inverted_index.measures | 12 |
| abstract_inverted_index.theorems | 149 |
| abstract_inverted_index.approach, | 80 |
| abstract_inverted_index.computing | 54 |
| abstract_inverted_index.connected | 188 |
| abstract_inverted_index.dependent | 61 |
| abstract_inverted_index.estimator | 107 |
| abstract_inverted_index.features. | 64 |
| abstract_inverted_index.framework | 114 |
| abstract_inverted_index.gaskets), | 197 |
| abstract_inverted_index.intuition | 163 |
| abstract_inverted_index.realistic | 212 |
| abstract_inverted_index.simplex), | 191 |
| abstract_inverted_index.weighting | 110 |
| abstract_inverted_index.applicable | 82 |
| abstract_inverted_index.approaches | 204 |
| abstract_inverted_index.connection | 6 |
| abstract_inverted_index.derivation | 117 |
| abstract_inverted_index.emphasized | 4 |
| abstract_inverted_index.estimation | 32 |
| abstract_inverted_index.estimator. | 125 |
| abstract_inverted_index.estimators | 152, 173 |
| abstract_inverted_index.importance | 11 |
| abstract_inverted_index.intensive. | 131 |
| abstract_inverted_index.physically | 95 |
| abstract_inverted_index.simulator. | 213 |
| abstract_inverted_index.strategies | 52 |
| abstract_inverted_index.(Sierpinski | 196 |
| abstract_inverted_index.U-statistic | 102, 113 |
| abstract_inverted_index.alternative | 51 |
| abstract_inverted_index.application | 209 |
| abstract_inverted_index.comparisons | 200 |
| abstract_inverted_index.consistency | 145 |
| abstract_inverted_index.constrained | 63, 184 |
| abstract_inverted_index.established | 41 |
| abstract_inverted_index.experiments | 181 |
| abstract_inverted_index.generalized | 70 |
| abstract_inverted_index.impractical | 91 |
| abstract_inverted_index.non-compact | 192 |
| abstract_inverted_index.sensitivity | 16 |
| abstract_inverted_index.challenging. | 36 |
| abstract_inverted_index.constrained. | 96 |
| abstract_inverted_index.correlations | 22 |
| abstract_inverted_index.derangements | 137 |
| abstract_inverted_index.henceforth). | 20 |
| abstract_inverted_index.increasingly | 178 |
| abstract_inverted_index.permutations | 29 |
| abstract_inverted_index.unrestricted | 28 |
| abstract_inverted_index.Additionally, | 37 |
| abstract_inverted_index.computational | 141, 168, 180 |
| abstract_inverted_index.dependencies, | 89 |
| abstract_inverted_index.non-Cartesian | 46 |
| abstract_inverted_index.non-connected | 194 |
| abstract_inverted_index.significantly | 139 |
| abstract_inverted_index.computationally | 130 |
| abstract_inverted_index.transformation. | 78 |
| abstract_inverted_index.Knothe-Rosenblatt | 77 |
| abstract_inverted_index.nearest-neighbour | 162 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.7878542 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |