PanIC: Consistent information criteria for general model selection problems Article Swipe
Summary Model selection is a ubiquitous problem that arises in the application of many statistical and machine learning methods. In the likelihood and related settings, it is typical to use the method of information criteria (ICs) to choose the most parsimonious among competing models by penalizing the likelihood‐based objective function. Theorems guaranteeing the consistency of ICs can often be difficult to verify and are often specific and bespoke. We present a set of results that guarantee consistency for a class of ICs, which we call PanIC (from the Greek root ‘ pan ’, meaning ‘ of everything ’), with easily verifiable regularity conditions. PanICs are applicable in any loss‐based learning problem and are not exclusive to likelihood problems. We illustrate the verification of regularity conditions for model selection problems regarding finite mixture models, least absolute deviation and support vector regression and principal component analysis, and demonstrate the effectiveness of PanICs for such problems via numerical simulations. Furthermore, we present new sufficient conditions for the consistency of BIC‐like estimators and provide comparisons of the BIC with PanIC.
Related Topics
Concepts
Consistency (knowledge bases)
Estimator
Mathematics
Model selection
Selection (genetic algorithm)
Set (abstract data type)
Mathematical optimization
Bespoke
Panic
Information Criteria
Econometrics
Computer science
Machine learning
Artificial intelligence
Statistics
Programming language
Political science
Anxiety
Law
Psychiatry
Psychology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/anzs.12426
- OA Status
- hybrid
- Cited By
- 1
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403990163
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403990163Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1111/anzs.12426Digital Object Identifier
- Title
-
PanIC: Consistent information criteria for general model selection problemsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-31Full publication date if available
- Authors
-
Hien D. NguyenList of authors in order
- Landing page
-
https://doi.org/10.1111/anzs.12426Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1111/anzs.12426Direct OA link when available
- Concepts
-
Consistency (knowledge bases), Estimator, Mathematics, Model selection, Selection (genetic algorithm), Set (abstract data type), Mathematical optimization, Bespoke, Panic, Information Criteria, Econometrics, Computer science, Machine learning, Artificial intelligence, Statistics, Programming language, Political science, Anxiety, Law, Psychiatry, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
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63Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 28, 36, 60, 115 |
| abstract_inverted_index.we | 83, 157 |
| abstract_inverted_index.BIC | 173 |
| abstract_inverted_index.ICs | 55 |
| abstract_inverted_index.and | 15, 22, 62, 66, 111, 136, 140, 144, 168 |
| abstract_inverted_index.any | 107 |
| abstract_inverted_index.are | 63, 104, 112 |
| abstract_inverted_index.can | 56 |
| abstract_inverted_index.for | 77, 125, 150, 162 |
| abstract_inverted_index.new | 159 |
| abstract_inverted_index.not | 113 |
| abstract_inverted_index.pan | 91 |
| abstract_inverted_index.set | 71 |
| abstract_inverted_index.the | 10, 20, 30, 38, 46, 52, 87, 120, 146, 163, 172 |
| abstract_inverted_index.use | 29 |
| abstract_inverted_index.via | 153 |
| abstract_inverted_index.‘ | 90, 94 |
| abstract_inverted_index.ICs, | 81 |
| abstract_inverted_index.call | 84 |
| abstract_inverted_index.many | 13 |
| abstract_inverted_index.most | 39 |
| abstract_inverted_index.root | 89 |
| abstract_inverted_index.such | 151 |
| abstract_inverted_index.that | 7, 74 |
| abstract_inverted_index.with | 98, 174 |
| abstract_inverted_index.’, | 92 |
| abstract_inverted_index.(ICs) | 35 |
| abstract_inverted_index.(from | 86 |
| abstract_inverted_index.Greek | 88 |
| abstract_inverted_index.Model | 1 |
| abstract_inverted_index.PanIC | 85 |
| abstract_inverted_index.among | 41 |
| abstract_inverted_index.class | 79 |
| abstract_inverted_index.least | 133 |
| abstract_inverted_index.model | 126 |
| abstract_inverted_index.often | 57, 64 |
| abstract_inverted_index.which | 82 |
| abstract_inverted_index.’), | 97 |
| abstract_inverted_index.PanIC. | 175 |
| abstract_inverted_index.PanICs | 103, 149 |
| abstract_inverted_index.arises | 8 |
| abstract_inverted_index.choose | 37 |
| abstract_inverted_index.easily | 99 |
| abstract_inverted_index.finite | 130 |
| abstract_inverted_index.method | 31 |
| abstract_inverted_index.models | 43 |
| abstract_inverted_index.vector | 138 |
| abstract_inverted_index.verify | 61 |
| abstract_inverted_index.Summary | 0 |
| abstract_inverted_index.machine | 16 |
| abstract_inverted_index.meaning | 93 |
| abstract_inverted_index.mixture | 131 |
| abstract_inverted_index.models, | 132 |
| abstract_inverted_index.present | 69, 158 |
| abstract_inverted_index.problem | 6, 110 |
| abstract_inverted_index.provide | 169 |
| abstract_inverted_index.related | 23 |
| abstract_inverted_index.results | 73 |
| abstract_inverted_index.support | 137 |
| abstract_inverted_index.typical | 27 |
| abstract_inverted_index.Theorems | 50 |
| abstract_inverted_index.absolute | 134 |
| abstract_inverted_index.bespoke. | 67 |
| abstract_inverted_index.criteria | 34 |
| abstract_inverted_index.learning | 17, 109 |
| abstract_inverted_index.methods. | 18 |
| abstract_inverted_index.problems | 128, 152 |
| abstract_inverted_index.specific | 65 |
| abstract_inverted_index.analysis, | 143 |
| abstract_inverted_index.competing | 42 |
| abstract_inverted_index.component | 142 |
| abstract_inverted_index.deviation | 135 |
| abstract_inverted_index.difficult | 59 |
| abstract_inverted_index.exclusive | 114 |
| abstract_inverted_index.function. | 49 |
| abstract_inverted_index.guarantee | 75 |
| abstract_inverted_index.numerical | 154 |
| abstract_inverted_index.objective | 48 |
| abstract_inverted_index.principal | 141 |
| abstract_inverted_index.problems. | 117 |
| abstract_inverted_index.regarding | 129 |
| abstract_inverted_index.selection | 2, 127 |
| abstract_inverted_index.settings, | 24 |
| abstract_inverted_index.BIC‐like | 166 |
| abstract_inverted_index.applicable | 105 |
| abstract_inverted_index.conditions | 124, 161 |
| abstract_inverted_index.estimators | 167 |
| abstract_inverted_index.everything | 96 |
| abstract_inverted_index.illustrate | 119 |
| abstract_inverted_index.likelihood | 21, 116 |
| abstract_inverted_index.penalizing | 45 |
| abstract_inverted_index.regression | 139 |
| abstract_inverted_index.regularity | 101, 123 |
| abstract_inverted_index.sufficient | 160 |
| abstract_inverted_index.ubiquitous | 5 |
| abstract_inverted_index.verifiable | 100 |
| abstract_inverted_index.application | 11 |
| abstract_inverted_index.comparisons | 170 |
| abstract_inverted_index.conditions. | 102 |
| abstract_inverted_index.consistency | 53, 76, 164 |
| abstract_inverted_index.demonstrate | 145 |
| abstract_inverted_index.information | 33 |
| abstract_inverted_index.statistical | 14 |
| abstract_inverted_index.Furthermore, | 156 |
| abstract_inverted_index.guaranteeing | 51 |
| abstract_inverted_index.loss‐based | 108 |
| abstract_inverted_index.parsimonious | 40 |
| abstract_inverted_index.simulations. | 155 |
| abstract_inverted_index.verification | 121 |
| abstract_inverted_index.effectiveness | 147 |
| abstract_inverted_index.likelihood‐based | 47 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5037340964 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I135598925, https://openalex.org/I196829312 |
| citation_normalized_percentile.value | 0.66829794 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |