Parametric and semiparametric approaches for copula-based regression estimation Article Swipe
Alam Ali
,
Ashok Kumar Pathak
,
Mohd. Arshad
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.15672/hujms.1359072
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.15672/hujms.1359072
Based on the normality assumption on dependent variable, regression analysis is one of the most popular statistical techniques for studying the dependence between response and explanatory variables. However, violation of this assumption in the data makes regression analysis inappropriate in several real life situations. Copula is a powerful tool for modeling multivariate data and have recently been employed in regression analysis. The key concept behind copula-based regression approach is to formulate conditional expectation in terms of copula density and marginal distributions. In this paper, we explore parametric and semiparametric estimations of the copula-based regression function. The maximum likelihood (ML), inference functions for margins (IFM), and pseudo maximum likelihood (PML) techniques are adopted here for estimation purposes. Extensive numerical experiments are performed to illustrate the performance of the proposed copula-based regression estimators under specified and misspecified scenarios of copulas and marginals. Finally, two real data applications are also presented to demonstrate the performance of the considered estimators.
Related Topics
Concepts
Copula (linguistics)
Mathematics
Estimator
Parametric statistics
Econometrics
Semiparametric regression
Regression analysis
Inference
Regression
Multivariate statistics
Semiparametric model
Statistics
Statistical inference
Conditional probability distribution
Computer science
Artificial intelligence
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.15672/hujms.1359072
- OA Status
- diamond
- Cited By
- 2
- References
- 27
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4401339564Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.15672/hujms.1359072Digital Object Identifier
- Title
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Parametric and semiparametric approaches for copula-based regression estimationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-05Full publication date if available
- Authors
-
Alam Ali, Ashok Kumar Pathak, Mohd. ArshadList of authors in order
- Landing page
-
https://doi.org/10.15672/hujms.1359072Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.15672/hujms.1359072Direct OA link when available
- Concepts
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Copula (linguistics), Mathematics, Estimator, Parametric statistics, Econometrics, Semiparametric regression, Regression analysis, Inference, Regression, Multivariate statistics, Semiparametric model, Statistics, Statistical inference, Conditional probability distribution, Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2Per-year citation counts (last 5 years)
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.life | 42 |
| abstract_inverted_index.most | 14 |
| abstract_inverted_index.real | 41, 142 |
| abstract_inverted_index.this | 30, 82 |
| abstract_inverted_index.tool | 48 |
| abstract_inverted_index.(ML), | 98 |
| abstract_inverted_index.(PML) | 108 |
| abstract_inverted_index.Based | 0 |
| abstract_inverted_index.makes | 35 |
| abstract_inverted_index.terms | 74 |
| abstract_inverted_index.under | 131 |
| abstract_inverted_index.(IFM), | 103 |
| abstract_inverted_index.Copula | 44 |
| abstract_inverted_index.behind | 64 |
| abstract_inverted_index.copula | 76 |
| abstract_inverted_index.paper, | 83 |
| abstract_inverted_index.pseudo | 105 |
| abstract_inverted_index.adopted | 111 |
| abstract_inverted_index.between | 22 |
| abstract_inverted_index.concept | 63 |
| abstract_inverted_index.copulas | 137 |
| abstract_inverted_index.density | 77 |
| abstract_inverted_index.explore | 85 |
| abstract_inverted_index.margins | 102 |
| abstract_inverted_index.maximum | 96, 106 |
| abstract_inverted_index.popular | 15 |
| abstract_inverted_index.several | 40 |
| abstract_inverted_index.Finally, | 140 |
| abstract_inverted_index.However, | 27 |
| abstract_inverted_index.analysis | 9, 37 |
| abstract_inverted_index.approach | 67 |
| abstract_inverted_index.employed | 57 |
| abstract_inverted_index.marginal | 79 |
| abstract_inverted_index.modeling | 50 |
| abstract_inverted_index.powerful | 47 |
| abstract_inverted_index.proposed | 127 |
| abstract_inverted_index.recently | 55 |
| abstract_inverted_index.response | 23 |
| abstract_inverted_index.studying | 19 |
| abstract_inverted_index.Extensive | 116 |
| abstract_inverted_index.analysis. | 60 |
| abstract_inverted_index.dependent | 6 |
| abstract_inverted_index.formulate | 70 |
| abstract_inverted_index.function. | 94 |
| abstract_inverted_index.functions | 100 |
| abstract_inverted_index.inference | 99 |
| abstract_inverted_index.normality | 3 |
| abstract_inverted_index.numerical | 117 |
| abstract_inverted_index.performed | 120 |
| abstract_inverted_index.presented | 147 |
| abstract_inverted_index.purposes. | 115 |
| abstract_inverted_index.scenarios | 135 |
| abstract_inverted_index.specified | 132 |
| abstract_inverted_index.variable, | 7 |
| abstract_inverted_index.violation | 28 |
| abstract_inverted_index.assumption | 4, 31 |
| abstract_inverted_index.considered | 154 |
| abstract_inverted_index.dependence | 21 |
| abstract_inverted_index.estimation | 114 |
| abstract_inverted_index.estimators | 130 |
| abstract_inverted_index.illustrate | 122 |
| abstract_inverted_index.likelihood | 97, 107 |
| abstract_inverted_index.marginals. | 139 |
| abstract_inverted_index.parametric | 86 |
| abstract_inverted_index.regression | 8, 36, 59, 66, 93, 129 |
| abstract_inverted_index.techniques | 17, 109 |
| abstract_inverted_index.variables. | 26 |
| abstract_inverted_index.conditional | 71 |
| abstract_inverted_index.demonstrate | 149 |
| abstract_inverted_index.estimations | 89 |
| abstract_inverted_index.estimators. | 155 |
| abstract_inverted_index.expectation | 72 |
| abstract_inverted_index.experiments | 118 |
| abstract_inverted_index.explanatory | 25 |
| abstract_inverted_index.performance | 124, 151 |
| abstract_inverted_index.situations. | 43 |
| abstract_inverted_index.statistical | 16 |
| abstract_inverted_index.applications | 144 |
| abstract_inverted_index.copula-based | 65, 92, 128 |
| abstract_inverted_index.misspecified | 134 |
| abstract_inverted_index.multivariate | 51 |
| abstract_inverted_index.inappropriate | 38 |
| abstract_inverted_index.distributions. | 80 |
| abstract_inverted_index.semiparametric | 88 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.83365751 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |